A method for allocating a system resource to an application, comprising: predicting, by a terminal based on a current status of the terminal, a target application to be used by a user; reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application, wherein the system resource required for running the target application is an idle system resource greater than or equal to a system resource requirement of the target application; and providing, by the terminal according to a resource allocation request of the target application, the reserved system resource for the target application to use; wherein the reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application comprises, when current idle system resources do not satisfy a resource requirement of the target application, releasing, by the terminal, some occupied system resources to obtain updated idle system resources, so as to allocate the system resource to the target application; wherein the method further comprises predicting, by the terminal, a use time point of the target application, wherein the reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application comprises reserving, by the terminal, the system resource for the target application before the use time point; wherein the predicting, by the terminal, a use time point of the target application comprises predicting, by the terminal, the use time point of the target application based on a machine learning model, wherein a model parameter of the machine learning model is determined based on historical data of using the target application by the user; and wherein the machine learning model comprises a hidden Markov model, and the predicting, by the terminal, a use time point of the target application comprises predicting the use time point of the target application by using the hidden Markov model, wherein the hidden Markov model is used to indicate a probability of using the target application in a target discrete time range. predicting, by a terminal based on a current status of the terminal, a target application to be used by a user; reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application, wherein the system resource required for running the target application is an idle system resource greater than or equal to a system resource requirement of the target application; and providing, by the terminal according to a resource allocation request of the target application, the reserved system resource for the target application to use; wherein the reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application comprises, when current idle system resources do not satisfy a resource requirement of the target application, releasing, by the terminal, some occupied system resources to obtain updated idle system resources, so as to allocate the system resource to the target application; wherein the method further comprises predicting, by the terminal, a use time point of the target application, wherein the reserving, by the terminal for the target application based on a prediction result, a system resource required for running the target application comprises reserving, by the terminal, the system resource for the target application before the use time point; wherein the predicting, by the terminal, a use time point of the target application comprises predicting, by the terminal, the use time point of the target application based on a machine learning model, wherein a model parameter of the machine learning model is determined based on historical data of using the target application by the user; and wherein the machine learning model comprises a hidden Markov model, and the predicting, by the terminal, a use time point of the target application comprises predicting the use time point of the target application by using the hidden Markov model, wherein the hidden Markov model is used to indicate a probability of using the target application in a target discrete time range.