With the development of information and communication technology, mobile phones have become an indispensable part of human daily life. Human activities have gradually extended from real space to cyberspace. The online behavior of cyberspace in the era of mobile Internet is inseparable from the travel behavior of real space. The current individual travel behavior predictive modeling is less concerned with the relationship between online behavior and travel behavior. A mobile phone user stay behavior prediction model based on the characteristics of online app usage behavior is proposed. Firstly, the time-space constraint is used to define the mobile phone user's stay behavior. Then, from multi-source data, the paper extracts the individual travel behavior's space-time preference, the app usage characteristics such as the APP combination, Internet traffic, Internet access times and other Internet behavior characteristics and weather information, etc. Feature engineering is done in a time and space crossing way, and the mobile phone user stay behavior prediction model with high interpretability from feature to model is constructed. We found the following from the experimental results: (1) The prediction accuracy of the model is 80.31%. After the integration of online behavior characteristics, weather and other external factors, the prediction accuracy is improved by 12.08%, compared with the model only using individual travel characteristics. (2) The prediction accuracy of the model is higher than that of Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT). And it is 1.23% lower than that of Random Forest (RF), but the model in this paper runs faster than RF, and the model solving process is easy to understand and interpretable. Besides, the first-order Markov model has a small amount of calculations and a fast running speed, but the accuracy is low. In general, the model in this paper has higher accuracy and fast running speed, which is more suitable for mobile phone user stay behavior prediction. (3) There is a big difference in the prediction accuracy of different users' stay behaviors prediction. The prediction accuracy of most users is concentrated between 70% and 90%. The highest prediction accuracy is 98.2%, and the worst prediction accuracy is 34.5%. (4) Among the app usage characteristics, the APP combination, Internet traffic and Internet access times contribute more to the prediction of mobile user stay behavior. The use of navigation, news and office apps has a particularly significant impact on the prediction results. In addition, comparing to the historical travel behavior characteristics, travel distance and activity radius in the current period have a stronger impact on the prediction of mobile phone user stay behavior.