As the development of social economy, the amount of data has been ever increasing. In order to dig out valuable information from the huge amount of data, it has become an important part in the field of data mining to predict the future through the potential law of historical data. This work studies the MLP, BP, and MLBP models and conducts error comparative analysis of the models, and then applies the optimal model to stock forecasting. The text uses the Tushare financial data interface provided by Python to crawl the stock daily trading data, and uses three models to analyze and process the stock trading data, adjusting some of the parameters continuously. The prediction results of each model algorithm are compared by using MSE error and finally an optimal prediction value is obtained.