Improve Classification Accuracy Research Articles

Seagrass classification using unsupervised curriculum learning (UCL)

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL.

Semantic Interaction Meta-Learning Based on Patch Matching Metric.

Metric-based meta-learning methods have demonstrated remarkable success in the domain of few-shot image classification. However, their performance is significantly contingent upon the choice of metric and the feature representation for the support classes. Current approaches, which predominantly rely on holistic image features, may inadvertently disregard critical details necessary for novel tasks, a phenomenon known as "supervision collapse". Moreover, relying solely on visual features to characterize support classes can prove to be insufficient, particularly in scenarios involving limited sample sizes. In this paper, we introduce an innovative framework named Patch Matching Metric-based Semantic Interaction Meta-Learning (PatSiML), designed to overcome these challenges. To counteract supervision collapse, we have developed a patch matching metric strategy based on the Transformer architecture to transform input images into a set of distinct patch embeddings. This approach dynamically creates task-specific embeddings, facilitated by a graph convolutional network, to formulate precise matching metrics between the support classes and the query image patches. To enhance the integration of semantic knowledge, we have also integrated a label-assisted channel semantic interaction strategy. This strategy merges word embeddings with patch-level visual features across the channel dimension, utilizing a sophisticated language model to combine semantic understanding with visual information. Our empirical findings across four diverse datasets reveal that the PatSiML method achieves a classification accuracy improvement of 0.65% to 21.15% over existing methodologies, underscoring its robustness and efficacy.

Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema

Diabetic Retinopathy (DR) and Diabetic Macular Edema (DME) are vision related complications prominently found in diabetic patients. The early identification of DR/DME grades facilitates the devising of an appropriate treatment plan, which ultimately prevents the probability of visual impairment in more than 90% of diabetic patients. Thereby, an automatic DR/DME grade detection approach is proposed in this work by utilizing image processing. In this work, the retinal fundus image provided as input is pre-processed using Discrete Wavelet Transform (DWT) with the aim of enhancing its visual quality. The precise detection of DR/DME is supported further with the application of suitable Artificial Neural Network (ANN) based segmentation technique. The segmented images are subsequently subjected to feature extraction using Adaptive Gabor Filter (AGF) and the feature selection using Random Forest (RF) technique. The former has excellent retinal vein recognition capability, while the latter has exceptional generalization capability. The RF approach also assists with the improvement of classification accuracy of Deep Convolutional Neural Network (CNN) classifier. Moreover, Chicken Swarm Algorithm (CSA) is used for further enhancing the classifier performance by optimizing the weights of both convolution and fully connected layer. The entire approach is validated for its accuracy in determination of grades of DR/DME using MATLAB software. The proposed DR/DME grade detection approach displays an excellent accuracy of 97.91%.

Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

Enhanced brain tumor classification using EfficientNetB0 and SVM with pareto search algorithm optimization

Classifying tumors by type, grade, and stage is crucial for treatment decisions and predicting outcomes. Deep learning, especially Convolutional Neural Networks (CNNs), has significantly advanced tumor classification by effectively analyzing complex patterns in magnetic resonance (MR) images. This work presents a hybrid image classification method using the EfficientNetB0 model and Support Vector Machine (SVM) to categorize brain MR images into pituitary tumor, glioma tumor, meningioma tumor, and normal brain. EfficientNetB0 model extracts deep features from the images, which are then classified by a linear SVM. To significantly enhance classification accuracy for brain images, we use the Pareto algorithm to determine the penalty parameter C for the linear SVM. The testing results showed that the proposed system achieved a classification accuracy of 99.30%, recall of 99.30%, precision of 99.30%, and F1-score of 99.30%, with a high specificity of 99.77%. These results demonstrate that the combination of the Pareto algorithm and SVM significantly contributes to improved classification accuracy for brain images.

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition

Stress is a prevalent issue in modern society which affects a large percentage of the population. Stress has negative effects on human daily lives such as reduced memory and concentration levels, forgetfulness, restlessness, and weakness. Although it is challenging to extinguish stress from human lives, monitoring and controlling its consequences can be implemented. Current methods for stress monitoring often lack interpretability of results and making it difficult to provide an interpretable system for immediate mental health counseling and intervention. Wearable devices are of growing interest due to the impact of the COVID-19 pandemic on mental health, as wearables provide monitoring of physiological parameters which helps in measuring stress levels. Here, an investigation on the feasibility of using wrist and chest-based multimodal physiological signals collected from wearables is executed for stress classification and identification of underlying modalities that impact the stress. Multimodalities from wrist and chest sensors are used to examine the three-class classification, baseline, stress and amusement. For improved and visualized effects, the multimodal signals from wearables were transformed into images. Then, a hybrid model was introduced, which comprised of attentive convolution neural network (CNN), transposed attentive CNN connections with long-short term memory (LSTM) and followed by attention layer for efficient feature extraction and improved classification accuracy. Results found that the proposed model with images provided 97 % accuracy and 96 % F1-score for chest wearable and 94 % accuracy and 93 % F1-score for wrist wearable data. Additionally, the post-hoc explainability approach (Local interpretable model agnostic applications, LIME) provided visual representations of contributing features from each signal for each class. LIME shows electrodermal activity (EDA), temperature (TEMP) and respiration (RESP) to be the significant factor in stress recognition from chest wearable and blood volume pulse (BVP) and EDA from wrist wearable. Additionally, increasing the number of features of the explainable model influences the modalities influence on the model explainability. The results establish a benchmark for explainable stress recognition using different sensor data.

A diffusion model multi-scale feature fusion network for imbalanced medical image classification research

Background and objectiveMedicine image classification are important methods of traditional medical image analysis, but the trainable data in medical image classification is highly imbalanced and the accuracy of medical image classification models is low. In view of the above two common problems in medical image classification. This study aims to: (i) effectively solve the problem of poor training effect caused by the imbalance of class imbalanced data sets. (ii) propose a network framework suitable for improving medical image classification results, which needs to be superior to existing methods. MethodsIn this paper, we put in the diffusion model multi-scale feature fusion network (DMSFF), which mainly uses the diffusion generation model to overcome imbalanced classes (DMOIC) on highly imbalanced medical image datasets. At the same time, it is processed according to the cropped image augmentation strategy through cropping (IASTC). Based on this, we use the new dataset to design a multi-scale feature fusion network (MSFF) that can fully utilize multiple hierarchical features. The DMSFF network can effectively solve the problems of small and imbalanced samples and low accuracy in medical image classification. ResultsWe evaluated the performance of the DMSFF network on highly imbalanced medical image classification datasets APTOS2019 and ISIC2018. Compared with other classification models, our proposed DMSFF network achieved significant improvements in classification accuracy and F1 score on two datasets, reaching 0.872, 0.731, and 0.906, 0.836, respectively. ConclusionsOur newly proposed DMSFF architecture outperforms existing methods on two datasets, and verifies the effectiveness of generative model inverse balance for imbalance class datasets and feature enhancement by multi-scale feature fusion. Further, the method can be applied to other class imbalanced data sets where the results will be improved.

MTLSC-Diff: Multitask learning with diffusion models for hyperspectral image super-resolution and classification

Hyperspectral image (HSIs) super-resolution (SR) can improve the spatial resolution of images for subsequent application tasks. In recent years, SR methods based on deep learning have gained widespread attention. However, most of the existing SR methods do not take into account the needs of specific application tasks when designing the network structure. These methods may not be able to efficiently generate high-quality images that satisfy the specific application tasks, leading to degradation of the performance of subsequent application tasks. To solve this problem, we propose a multi-task learning architecture based on the diffusion model, namely MTLSC-Diff. MTLSC-Diff combines the SR network and the classification network in a multi-task learning manner on the basis of the diffusion model. MTLSC-Diff achieves mutual guidance of the two tasks by iterating the image super-resolution and classification tasks, thus gradually reconstructing high-quality images and improving classification accuracy. The guided operations for each time step are performed by the specially designed Mutual-Guidance SR-Classification Synergy Module (M-GSCS). M-GSCS refines the multi-scale image obtained at the previous time step and uses the predicted high spatial resolution image for classification. Meanwhile, a class-guided SR dynamic refinement strategy (C-GSR) is proposed in M-GSCS, which uses multi-scale classification results to guide target scale images to learn new knowledge to further reconstruct high-quality images. Experimental results on relevant datasets show that our method significantly improves the super-resolution performance as well as the classification performance.

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney function and ultimately leads to complications such as chronic kidney disease, kidney failure, and other renal disorders. Combining patient metadata with computed tomography (CT) images is essential to accurately and timely diagnosing such complications. Deep Neural Networks (DNNs) have transformed medical fields by providing high accuracy in complex tasks. However, the high computational cost of these models is a significant challenge, particularly in real-time applications. This paper proposed SpinalZFNet, a hybrid deep learning approach that integrates the architectural strengths of Spinal Network (SpinalNet) with the feature extraction capabilities of Zeiler and Fergus Network (ZFNet) to classify kidney disease accurately using CT images. This unique combination enhanced feature analysis, significantly improving classification accuracy while reducing the computational overhead. At first, the acquired CT images are pre-processed using a median filter, and the pre-processed image is segmented using Efficient Neural Network (ENet). Later, the images are augmented, and different features are extracted from the augmented CT images. The extracted features finally classify the kidney disease into normal, tumor, cyst, and stone using the proposed SpinalZFNet model. The SpinalZFNet outperformed other models, with 99.9% sensitivity, 99.5% specificity, precision 99.6%, 99.8% accuracy, and 99.7% F1-Score in classifying kidney disease.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

Smooth Ordered Weighted Averaging operators

The study demonstrates the application of OWA operators to binary and multiclass classification problems and seeks a way to improve classification accuracy using smoothing methods. OWA operators are used to aggregate class membership probabilities obtained from individual classifiers. Smoothing methods inspired by Newton-Cotes quadratures are applied before the aggregation step to improve the quality of the final results. Moreover, several sets of weights are used for OWA operators, including sets of weights based on the accuracy of individual classifiers. The experiments are conducted on 20 datasets, from which 7 are designed for binary classification and the rest are for multiclass classification. A comparison of the average accuracy for different sets of weights is shown. On the basis of experimental results, smoothing methods that significantly improve classification accuracy are identified.

Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

AN EMOTION ANALYSIS METHOD THAT INTEGRATES EEG SIGNALS AND MUSIC THERAPY USING A TRANSFORMER MODEL

The need for sentiment analysis in the mental health field is increasing, and electroencephalogram (EEG) signals and music therapy have attracted extensive attention from researchers as breakthrough ideas. However, the existing methods still face the challenge of integrating temporal and spatial features when combining these two types, especially when considering the volume conduction differences among multichannel EEG signals and the different response speeds of subjects; moreover, the precision and accuracy of emotion analysis have yet to be improved. To solve this problem, we integrate the idea of top-k selection into the classic transformer model and construct a novel top-k sparse transformer model. This model captures emotion-related information in a finer way by selecting k data segments from an EEG signal with distinct signal features. However, this optimization process is not without its challenges, and we need to balance the selected k values to ensure that the important features are preserved while avoiding excessive information loss. Experiments conducted on the DEAP dataset demonstrate that our approach achieves significant improvements over other models. By enhancing the sensitivity of the model to the emotion-related information contained in EEG signals, our method achieves an overall emotion classification accuracy improvement and obtains satisfactory results when classifying different emotion dimensions. This study fills a research gap in the field of sentiment analysis involving EEG signals and music therapy, provides a novel and effective method, and is expected to lead to new ideas regarding the application of deep learning in sentiment analysis.

Real-time thermography for breast cancer detection with deep learning

In this study, we propose a framework that enhances breast cancer classification accuracy by preserving spatial features and leveraging in situ cooling support. The framework utilizes real-time thermography video streaming for early breast cancer detection using Deep Learning models. Inception v3, Inception v4, and a modified Inception Mv4 were developed using MATLAB 2019. However, the thermal camera was connected to a mobile phone to capture images of the breast area for classification of normal and abnormal breast. This study’s training dataset included 1000 thermal images, where a FLIR One Pro thermal camera connected to a mobile device was used for the imaging process. Of the 1000 images obtained, 700 images were considered for the normal breast thermography class while the 300 images were suitable for the abnormal class. We evaluate Deep Convolutional Neural Network models, such as Inception v3, Inception v4, and a modified Inception Mv4. Our results demonstrate that Inception Mv4, with real-time video streaming, efficiently detects even the slightest temperature contrast in breast tissue sequences achieving a 99.748% accuracy in comparison to a 99.712% and 96.8% for Inception v4 and v3, respectively. The use of in situ cooling gel further enhances image acquisition efficiency and detection accuracy. Interestingly, increasing the tumor surface temperature by 0.1% leads to an average 7% improvement in detection and classification accuracy. Our findings support the effectiveness of Inception Mv4 for real-time breast cancer detection, especially when combined with in situ cooling gel and varying tumor temperatures. In conclusion, future research directions should focus on incorporating thermal video clips into the thermal images database, utilizing high-quality thermal cameras, and exploring alternative Deep Learning models for improved breast cancer detection.

Single-objective and multi-objective mixed-variable grey wolf optimizer for joint feature selection and classifier parameter tuning

Feature selection plays an essential role in data preprocessing, which can extract valuable information from extensive data, thereby enhancing the performance of machine learning classification. However, existing feature selection methods primarily focus on selecting feature subsets without considering the impact of classifier parameters on the optimal subset. Different from these works, this paper considers jointly optimizing the feature subset and classifier parameters to minimize the number of features and achieve a low classification error rate. Since feature selection is an optimization problem with binary solution space while classifier parameters involve both continuous and discrete variables, our formulated problem becomes a complex multi-objective mixed-variable problem. To address this challenge, we consider a single-objective optimization method and a multi-objective optimization approach. Specifically, in the single-objective optimization method, we adopt the linear weight method to convert our multiple objectives into a fitness function and then propose a mixed-variable grey wolf optimizer (MGWO) to optimize the function. The proposed MGWO introduces Chaos-Faure initialization, Log convergence factor adjustment, and optimal solution adaptive update operators to enhance its adaptability and balance the global and local search of the algorithm. Subsequently, an improved multi-objective grey wolf optimizer (IMOGWO) is introduced to directly address the problem. The proposed IMOGWO introduces improved initialization, local search, and binary variable mutation operators to balance its exploration and exploitation abilities, making it more suitable for our mixed-variable problem. Extensive simulation results show that our MGWO and IMOGWO outperform recent and classic baselines. Moreover, we also find that jointly optimizing classifier parameters can significantly improve classification accuracy.

A deep learning based experimental framework for automatic staging of pressure ulcers from thermal images

ABSTRACT Pressure ulcers can be life-threatening, and accurate staging is crucial for proper treatment. Manual methods for ulcer detection and staging are subjective and prone to errors. Since the wound temperature holds valuable and informative data, we propose thermal imaging as a tool to detect the stages of pressure ulcers automatically. This paper implements a deep learning-based framework for the automatic detection and classification of pressure ulcer stages 2–4 using thermal images. 450 thermal images were used to train the classification model. The framework combines ‘R50-FPN’, retrained using transfer learning, for ulcer detection and cropping. An ensemble model is used for ulcer stage classification. The model was trained on a dataset consisting of 68 original images and their augmentations. The proposed method achieves a mAP for object detection of 0.70 and an ulcer stage classification accuracy of 0.9545 during evaluation. Class activation maps were also employed to study the model performance further, indicating that the trained model focuses on the wound periphery and the wound bed for classification. The results show a significant improvement in classification accuracy when compared with related works demonstrating the potential of deep learning-based thermal imaging as an objective method for the automated staging of pressure ulcers.

Research on sports image classification method based on SE-RES-CNN model

As computer image processing and digital technologies advance, creating an efficient method for classifying sports images is crucial for the rapid retrieval and management of large image datasets. Traditional manual methods for classifying sports images are impractical for large-scale data and often inaccurate when distinguishing similar images. This paper introduces an SE module that adaptively adjusts the weights of input feature mapping channels, and a Res module that excels in deep feature extraction, preventing gradient vanishing, multi-scale processing, and enhancing generalization in image recognition. Through extensive experimentation on network structure adjustments, the SE-RES-CNN neural network model is applied to sports image classification. The model is trained on a sports image classification dataset from Kaggle, alongside VGG-16 and ResNet50 models. Training results show that the proposed SE-RES-CNN model improves classification accuracy by approximately 5% compared to VGG-16 and ResNet50 models. Testing revealed that the SE-RES-CNN model classifies 100 out of 500 sports images in 6 s, achieving an accuracy rate of up to 98% and a single prediction time of 0.012 s. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency. This validates the model's accuracy and effectiveness, significantly enhancing sports image retrieval and classification efficiency.

Utilizing Large Language Models to Illustrate Constraints for Construction Planning

Effective construction project planning relies on addressing constraints related to materials, labor, equipment, and others. Planning meetings are typical venues for stakeholders to identify, communicate, and remove constraints. However, a critical gap exists in lacking an automated approach to identify, classify, analyze, and track constraint discussions during onsite planning meetings. Therefore, this research aims to 1. develop a natural language processing model to classify constraints in meeting discussions; 2. uncover the discussion patterns of managers and foremen regarding various constraints; and 3. extract the root causes for constraints, evaluate their impacts, and prepare managers to develop practical solutions for constraint removal. This research collected meeting transcripts from 94 onsite planning meetings of a building project, spanning 263,836 words. Next, this research leveraged a general pretrained transformer (GPT) to segment discussion dialogs into topics. A Bidirectional Encoder Representations from Transformers (BERT)-based model was developed to categorize constraint types for each topic. The constraint patterns among meeting attendees were assessed. Furthermore, a GPT-based tool was devised to track root causes, impacts, and solutions for various constraints. Test results revealed an 8.8% improvement in constraint classification accuracy compared with the traditional classification model. An occupational characteristic in constraint discussion was observed in that the management team tended to balance their focus on various constraints, while foremen concentrated on more practical issues. This research contributes to the body of knowledge by leveraging language models to analyze construction planning meetings. The findings facilitate project managers in establishing constraint logs for diagnosing and prognosticating planning issues.

WAL-Net: Weakly supervised auxiliary task learning network for carotid plaques classification

The classification of carotid artery ultrasound images is a crucial step for diagnosing carotid plaques, holding significant clinical relevance for predicting the risk of stroke. Recent research suggests that utilizing supervised plaque segmentation as an auxiliary task for classification can enhance performance by leveraging the correlation between segmentation and classification tasks. However, this approach relies on obtaining a substantial amount of challenging-to-acquire segmentation annotations. This paper proposes a novel weakly supervised auxiliary task learning network model (WAL-Net) to explore the interdependence between carotid plaque classification and segmentation tasks. The plaque classification task is the primary one, while the plaque segmentation task serves as the auxiliary one, providing valuable information to enhance the performance of the primary task. Weakly supervised learning is adopted in the auxiliary task in which the segmentation masks are not provided. Experiments and evaluations are conducted on a dataset comprising 1270 carotid plaque ultrasound images from Wuhan University Zhongnan Hospital. Results indicate that WAL-Net achieved an approximately 1.3% improvement in carotid plaque classification accuracy compared to the baseline network. Specifically, the accuracy of mixed-echoic plaques classification increased by approximately 3.3%, demonstrating the effectiveness of WAL-Net.

LSTM-Based Forum Topic Classification Model

With the rapid development of information technology, forums have become an important platform for information exchange. However, the manual classification of forum topics consumes a significant amount of human resources and is prone to classification errors. To address this issue, this study proposes a forum topic classification model based on Long Short-Term Memory (LSTM) networks. By leveraging LSTM’s capability in text processing, the accuracy and efficiency of topic classification are significantly improved. This study used approximately 68,000 entries from 29 topic categories, scraped from Zhihu, for the experiments. Preprocessing steps such as text cleaning, tokenization, and word vectorization were performed, and a classification model with LSTM and Dropout layers was designed. The experimental results indicate that the model performs well in most topic classifications, although overfitting remains an issue for certain categories. The paper concludes by summarizing the advantages and limitations of the model and discusses the potential for improving classification accuracy through increasing data volume and optimizing the model in the future.