A method for preloading an application, being applied to a terminal device, the method comprising: acquiring current state feature information of the terminal device, when an application preloading prediction event is detected to be triggered ; inputting the current state feature information into a plurality of decision tree prediction models each corresponding to an application in a preset application set , to cause each of the decision tree prediction models to output a probability value; wherein for each decision tree prediction model, the decision tree prediction model is caused to output a probability value indicating a probability that its corresponding application becomes a next application used by a user; the decision tree prediction model is generated based on a usage regularity of its corresponding application, wherein the usage regularity corresponds to historical state feature information of the terminal device; acquiring the probability values output by the plurality of decision tree prediction models, comprising: acquiring a probability value output from a leaf node that matches the current state feature information in each decision tree prediction model ; predicting target applications to be initiated according to the probability values output by the decision tree prediction models , comprising: selecting N probability values having the greatest values from the plurality of probability values , wherein N is a positive integer greater than or equal to one; and determining applications corresponding to the selected N probability values as the target applications to be initiated ; and preloading the target applications . acquiring current state feature information of the terminal device, when an application preloading prediction event is detected to be triggered ; inputting the current state feature information into a plurality of decision tree prediction models each corresponding to an application in a preset application set , to cause each of the decision tree prediction models to output a probability value; wherein for each decision tree prediction model, the decision tree prediction model is caused to output a probability value indicating a probability that its corresponding application becomes a next application used by a user; the decision tree prediction model is generated based on a usage regularity of its corresponding application, wherein the usage regularity corresponds to historical state feature information of the terminal device; acquiring the probability values output by the plurality of decision tree prediction models, comprising: acquiring a probability value output from a leaf node that matches the current state feature information in each decision tree prediction model ; predicting target applications to be initiated according to the probability values output by the decision tree prediction models , comprising: selecting N probability values having the greatest values from the plurality of probability values , wherein N is a positive integer greater than or equal to one; and determining applications corresponding to the selected N probability values as the target applications to be initiated ; and selecting N probability values having the greatest values from the plurality of probability values , wherein N is a positive integer greater than or equal to one; and determining applications corresponding to the selected N probability values as the target applications to be initiated ; and preloading the target applications . <EOS>