For a long time, investors have always been influenced by their own experience and investment expert advice. Machine Learning method in Quantitative Investment is an advanced method that replaces subjective judgments with artificial intelligence models to improve transaction accuracy. The authors have implemented a composite stock price prediction model based on multi-layer training networks, which is more suitable for predicting future stock prices compared to traditional methods. This model starts from the time series of stock data and deeply integrates the characteristics of stocks with artificial intelligence. The deep training sub-model is a combination of machine learning models and traditional statistical methods. This unique design cleverly solves the one-sidedness of machine learning methods and the petrification of classical financial methods. Through comparative experiments on multiple prediction models within the same time period, the model proposed in this paper has been proven to be the most effective model.