Prediction of engineering investment spillover effect based on neural network

Engineering investment is the basic investment of the whole national economic development. When the project investor obtains its expected return, it may have other beneficial benefits for social organizations or people outside the subject, but the investor cannot obtain such benefits. Spillover usually occurs from three aspects: economy, technology and knowledge. The spillover effect of project investment usually brings obvious spillover effect, which has positive benefits to society, but may also produce unfavorable factors. Therefore, it is necessary to predict the project investment spillover. When it is predicted that the investment spillover will have more favorable benefits, the preparation of relevant investment funds can be started, and when it is predicted that there will be unfavorable spillover benefits, the investment in related engineering projects will be terminated. Project investment spillover effects usually have specific rules. On the basis of summarizing and analyzing historical project investment spillover effects, the specific situation of its spillover effects can be obtained, and then the rules can be learned in combination with specific algorithms to complete the project investment spillover effects. predict. The purpose of this paper is to provide investors and institutions with a valuable investment forecasting reference method, combined with the relevant theories of the investment value of engineering market-oriented enterprises, using quantitative analysis methods and quantitative analysis methods, so as to provide an investment based on data and algorithms. The spillover value forecast method supports and promotes the development and construction of national key projects. Based on the completion of the entire prediction model, this paper uses the particle swarm optimization method of the deep neural network model process studied in this paper, and based on the relevant data of 284 historical engineering investment overflow cases, the algorithm is trained and output, and then the investment overflow of each project is obtained. The relative score of the predictions, and analyzing this overflow prediction. Through the obtained comprehensive prediction score and according to the result analysis. Corresponding conclusions and future development directions are put forward to provide theoretical guidance for investors and institutions to invest in investment direction and estimate investment spillover effects.