A data processing method performed by a terminal, comprising: obtaining target perception data, wherein the target perception data is any type of the following data: image data; determining a target scenario to which the target perception data belongs; determining a target perception model corresponding to the target scenario, where the target perception model is used to recognize objects in images; and computing an object recognition result of the target perception data by using the target perception model to process the target perception data, wherein the determining a target perception model corresponding to the target scenario comprises: when it is determined that a perception model library used to store perception models configured to recognize objects in images in various given scenarios, which is locally pre-stored in the terminal, has no perception model corresponding to the target scenario, sending, to a server, a second request used to request the perception model corresponding to the target scenario, wherein the perception models respectively comprise a convolutional neural network or a deep learning network and are respectively corresponding to different scenarios as being obtained through training according to a perception data sample of a respective scenario comprising at least a part of perception data, which is image data, that has scenario annotation information and object annotation information; and receiving the target perception model that is corresponding to the target scenario and sent by the server according to the second request. obtaining target perception data, wherein the target perception data is any type of the following data: image data; determining a target scenario to which the target perception data belongs; determining a target perception model corresponding to the target scenario, where the target perception model is used to recognize objects in images; and computing an object recognition result of the target perception data by using the target perception model to process the target perception data, wherein the determining a target perception model corresponding to the target scenario comprises: when it is determined that a perception model library used to store perception models configured to recognize objects in images in various given scenarios, which is locally pre-stored in the terminal, has no perception model corresponding to the target scenario, sending, to a server, a second request used to request the perception model corresponding to the target scenario, wherein the perception models respectively comprise a convolutional neural network or a deep learning network and are respectively corresponding to different scenarios as being obtained through training according to a perception data sample of a respective scenario comprising at least a part of perception data, which is image data, that has scenario annotation information and object annotation information; and receiving the target perception model that is corresponding to the target scenario and sent by the server according to the second request. when it is determined that a perception model library used to store perception models configured to recognize objects in images in various given scenarios, which is locally pre-stored in the terminal, has no perception model corresponding to the target scenario, sending, to a server, a second request used to request the perception model corresponding to the target scenario, wherein the perception models respectively comprise a convolutional neural network or a deep learning network and are respectively corresponding to different scenarios as being obtained through training according to a perception data sample of a respective scenario comprising at least a part of perception data, which is image data, that has scenario annotation information and object annotation information; and receiving the target perception model that is corresponding to the target scenario and sent by the server according to the second request.