In the context of digital informatization, the Internet is changing the way of human existence. The rapid development of the Internet has promoted the use of smartphones in people’s daily lives, and at the same time, a large number of applications running on different operating system environments have appeared on the market. Predicting the duration of application usage is crucial for the management planning of related companies and the good life of users. In this work, a dataset containing time series of user application usage information is considered and the problem of “application usage” forecast is being addressed. The dataset used in this work is based on reliable and realistic user records of the usage of the application. Firstly, this paper investigates suitable forecast models for application development on the applied user usage time dataset, which includes neural network algorithms and ensemble algorithms, among others. Then, an Explainable Artificial Intelligence Approach (SHAP) is introduced to explain the selected optimal forecast models, thus enhancing user trust of the forecasting models. The forecast results show that the ensemble models perform better in the time series dataset of user application usage information, especially LightGBM has more obvious advantages. Explanation results show that the frequency of use of the target variables, category and lagged nature are important features in the forecast of the application dataset.