Abstract
Automatic classification is one of the hot topics in the field of information retrieval and natural language processing, but it still faces many problems to be solved. The classic automated classification approach has a sluggish classification speed and poor processing accuracy for resources with a large quantity of data. Based on this, an automated classification approach based on the integration of various neural networks for fundamental nursing teaching materials was presented. The automatic classification method of teaching resources was designed by extracting the characteristics of teaching resources, establishing the model of multiple neural network integration, and designing the classification index of basic nursing teaching resources. The experimental findings suggest that this technique has higher chi-square test parameters and better outcomes for the automated classification of large instructional materials than the classic rough set automatic classification method.
Highlights
Mathematical Problems in Engineering in recent years. e Internet has a large amount of unique information resources of different forms, such as text, voice, picture, video, and so on
Extracting the Characteristics of Teaching Resources. e indexing words obtained by text expression are called feature terms or attributes in machine learning, which generally has the problem of large dimension
Feature selection refers to the selection of the important indexing words that can express the topic content of the literature from the initial feature item set. e following are some of the most often used feature selection techniques. e quantity of literature in the training set that include a certain term is referred to as the literature frequency selection feature approach. e hypothesis assumption of the literature frequency approach is that terms with low literature frequency give little useful information for classification prediction and have minimal influence on overall classifier performance; they may be discarded
Summary
Separately for each word in the training set. e selection of feature words and weight computation are the most challenging aspects of text feature extraction. The words whose average value is greater than the threshold value will be selected as the feature item of classification training. Feature selection generally ignores the semantic relationship between indexing words. E feature dimension reduction method based on multivariate neural network fusion has achieved a better effect than the traditional one. It is widely used in feature extraction, mainly used in feature extraction of dynamic video texture, audio, and image, and used in medical diagnosis. Deep neural networks may be separated into H classes, generative models, discriminative models, and mixed models based on their structure and principles. In formula, Z(θ) represents the normalization factor and e represents the input layer constant
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