A method performed by a mobile terminal for processing user information, comprising: Collecting terminal information, wherein the terminal information comprises a position of the mobile terminal; obtaining prior probability information of different types of user activities at current time and in the position in a human activity distribution database; and recognizing or forecasting, according to the prior probability information and the terminal information, an activity of a terminal user using the mobile terminal, wherein the recognizing or forecasting comprises: inputting the prior probability information and the terminal information to a pre-trained user activity classification model and training the user activity classification model by using a preset algorithm, so as to obtain an activity recognition result of the terminal user; inputting the activity recognition result and the terminal information to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain an activity forecast result of the terminal user; and inputting a next prior probability information and the activity forecast result to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain another activity forecast result of the terminal user, wherein the next prior probability information is prior probability of various activities to be performed next. Collecting terminal information, wherein the terminal information comprises a position of the mobile terminal; obtaining prior probability information of different types of user activities at current time and in the position in a human activity distribution database; and recognizing or forecasting, according to the prior probability information and the terminal information, an activity of a terminal user using the mobile terminal, wherein the recognizing or forecasting comprises: inputting the prior probability information and the terminal information to a pre-trained user activity classification model and training the user activity classification model by using a preset algorithm, so as to obtain an activity recognition result of the terminal user; inputting the activity recognition result and the terminal information to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain an activity forecast result of the terminal user; and inputting a next prior probability information and the activity forecast result to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain another activity forecast result of the terminal user, wherein the next prior probability information is prior probability of various activities to be performed next. inputting the prior probability information and the terminal information to a pre-trained user activity classification model and training the user activity classification model by using a preset algorithm, so as to obtain an activity recognition result of the terminal user; inputting the activity recognition result and the terminal information to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain an activity forecast result of the terminal user; and inputting a next prior probability information and the activity forecast result to a pre-trained user activity forecast model and training the user activity forecast model by using a preset algorithm, so as to obtain another activity forecast result of the terminal user, wherein the next prior probability information is prior probability of various activities to be performed next.