In the big data era, the studies of the quantitative stock selection strategy based on machine learning are becoming more and more popular. Most of existing studies focus on short-term strategies, and few on the medium-term or long-term strategies. Moreover, many scholars tend to transform the problem of predicting changes of stock prices into the binary classification problem, which makes it difficult to earn steady abnormal returns. Therefore, it is extraordinary meaningful to study effective quantitative investment strategies. In this article, we propose the modified BP neural network combining AdaBoost algorithm (the modified BP_AdaBoost) and apply it into the quantitative stock selection. We carry out empirical studies about medium-term and long-term price changes in the A share market of our country, construct the factor pool and check the performances of the modified BP_AdaBoost.