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

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.