An application preloading method comprising: obtaining application usage state information of a terminal and contextual information of the terminal; wherein the application usage state information comprises information of an application currently in use, or information indicating that no application is currently in use; inputting the application usage state information and the contextual information into a pre-generated prediction model, the prediction model being configured to predict application startup and to calculate at least one prediction value for the application startup, wherein the prediction model is pre-generated according to usage association information of applications within a predetermined time period and contextual information of the terminal corresponding to the usage association information; and determining an application to be started according to the at least one prediction value, and preloading the application to be started; wherein the method further comprises generating the prediction model for predicting the application startup, comprising: obtaining a user behavior sample within the predetermined time period, wherein the user behavior sample comprises usage association information of at least two applications, wherein the usage association information comprises application usage state information at each sampling point in time within the predetermined time period and a time sequence of using applications within the predetermined time period; extracting the contextual information of the terminal corresponding to the usage association information of the at least two applications at each sampling point in time within the predetermined time period; and inputting the usage association information and the contextual information as training data into a predetermined algorithm model, and obtaining the prediction model for predicting the application startup through training. obtaining application usage state information of a terminal and contextual information of the terminal; wherein the application usage state information comprises information of an application currently in use, or information indicating that no application is currently in use; inputting the application usage state information and the contextual information into a pre-generated prediction model, the prediction model being configured to predict application startup and to calculate at least one prediction value for the application startup, wherein the prediction model is pre-generated according to usage association information of applications within a predetermined time period and contextual information of the terminal corresponding to the usage association information; and determining an application to be started according to the at least one prediction value, and preloading the application to be started; wherein the method further comprises generating the prediction model for predicting the application startup, comprising: obtaining a user behavior sample within the predetermined time period, wherein the user behavior sample comprises usage association information of at least two applications, wherein the usage association information comprises application usage state information at each sampling point in time within the predetermined time period and a time sequence of using applications within the predetermined time period; extracting the contextual information of the terminal corresponding to the usage association information of the at least two applications at each sampling point in time within the predetermined time period; and inputting the usage association information and the contextual information as training data into a predetermined algorithm model, and obtaining the prediction model for predicting the application startup through training. obtaining a user behavior sample within the predetermined time period, wherein the user behavior sample comprises usage association information of at least two applications, wherein the usage association information comprises application usage state information at each sampling point in time within the predetermined time period and a time sequence of using applications within the predetermined time period; extracting the contextual information of the terminal corresponding to the usage association information of the at least two applications at each sampling point in time within the predetermined time period; and inputting the usage association information and the contextual information as training data into a predetermined algorithm model, and obtaining the prediction model for predicting the application startup through training.