In this paper, we integrated machine learning into the field of quantitative investment and established a set of automatic stock selection and investment timing models. Based on the validity test of factors, a multi-factor stock selection model was established to select stocks with the highest investment value to create a stock pool. By comparing the cumulative returns and the overall market returns of different timing signals over the same time period, both the decision tree and the long short-term memory (LSTM) models had great results. Finally, empirical research was reported to show that it is a good combination to introduce machine learning algorithms into quantitative timing.