Research on Performance Prediction of Technological Innovation Enterprises Based on Deep Learning

Huan Liu,Yuanpeng Zhang

doi:10.1155/2021/1682163

Abstract

High-tech enterprises are the leaders in promoting economic development. The study of the relationship between their scientific and technological innovation capabilities and corporate performance is of far-reaching practical significance for guiding companies to formulate independent innovation strategies scientifically, improving their independent innovation capabilities, and promoting further transformation into an innovative country. In view of the large-scale technological innovation enterprise network, the traditional technological innovation enterprise performance prediction method cannot fully reflect the real-time technological innovation enterprise status. Aiming at the deficiencies of the existing short-term technology innovation enterprise forecasting methods, this paper proposes a technology innovation enterprise performance forecasting method based on deep learning. I analyze the temporal and spatial characteristics of the data of technological innovation enterprises and divide the data according to the temporal characteristics of technological innovation enterprises. According to the spatial relevance of technological innovation enterprises, grouping is carried out by setting different correlation coefficient thresholds. The method of spectral decomposition is used to divide the data of scientific and technological innovation enterprises into trend items and random fluctuation items, to decompose the matrix of scientific and technological innovation enterprises, and to construct a compressed matrix using correlation. Using the deep belief network model in deep learning combined with support vector regression to establish a prediction model for technological innovation enterprises, this paper proposes a convolutional neural network model for performance prediction of scientific and technological innovation enterprises. Through the convolution operation and subsampling operation based on the concept of local window, the feature learning from the local to the whole is completed. This article uses the Naive Bayes model, logistic regression model, support vector regression model, and other mainstream methods to predict and compare the performance of technological innovation enterprises. I use the dropout method to reduce the impact of overfitting during training. The experimental results show that the deep neural network model method used in this article can achieve better prediction results than mainstream methods under the same characteristics. The experimental results on the data set confirm that the method of performance prediction of technology innovation enterprises based on deep learning used in this paper can effectively improve the results of performance prediction of technology innovation enterprises.

Highlights

Deep learning is an extension and expansion of the traditional artificial neural network field and a successful application of the bionics of the human brain
Third: through comparative experiments under the same characteristics, it is verified that the convolutional neural network is effective for the performance prediction of search advertising technology innovation enterprises, which shows that the convolutional layer and subsampling layer are effective for feature learning
In the table of scientific and technological activities of high-tech enterprises, there are special indicators for research and experimental development (R&D) personnel. Those employees who directly participate in project activities and related management and service personnel are included, and they are converted according to workload to ensure that the R&D personnel accounting caliber is consistent with international standards

Summary

Introduction

Deep learning is an extension and expansion of the traditional artificial neural network field and a successful application of the bionics of the human brain. Compared with shallow neural networks, deep learning has fewer parameters when fitting the nonlinear functional relationships of data, stronger representation ability, and selected data features [2] It is more precise, so that the results of data processing are more accurate and more in line with the original characteristics of the data. The technical contributions of this article can be summarized as follows: First: this article introduces the compression processing algorithm of scientific and technological innovation enterprise data and constructs a compression matrix using relevant analysis theories It describes the proposed technology innovation enterprise Deep Belief Network-Support Vector Regression (DBN-SVR) prediction model, respectively, and illustrates the algorithm process of the DBN model, the SVR classifier, and the prediction model used. Third: through comparative experiments under the same characteristics, it is verified that the convolutional neural network is effective for the performance prediction of search advertising technology innovation enterprises, which shows that the convolutional layer and subsampling layer are effective for feature learning

Related Work

Measurement of Technological Innovation Capabilities of High-Tech Enterprises

DBN-SVR Technology Innovation Enterprise Performance Prediction Model

Experimental Simulation Analysis

Findings

Conclusion