Research on University Book Purchasing Model Based on Genetic-Neural Network Algorithm

This paper introduces the basic principle of neural network and its data processing flow, focusing on the analysis of the network in library information evaluation and evaluation, forecasting and modeling applications. In order to solve the problem that BP Algorithm is easy to fall into local optimization and low training efficiency, a genetic algorithm with good global optimization ability is introduced in the model of book purchasing of University Library based on network neural algorithm, to improve the threshold and weight of BP Neural Network, in order to find the best combination of kernel function type, penalty factor and kernel parameter, and to improve the prediction effect of the model. And then improve the training efficiency of the Algorithm. By optimizing the method of neural network with genetic algorithm, this paper Constructs a book purchasing recommendation model based on genetic neural network to judge the books with high demand from readers, and then generates the recommended purchasing sequence. In the case of limited input, priority procurement needs higher professional books, so that the cost of input is optimized.