An action recognition method, executable by a computer device, the method comprising: obtaining image data of a plurality of temporal frames in video data, and obtaining original sub-feature maps of the image data of each of the temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, for each of the temporal frames as a target temporal frame, motion information weights respectively for the convolutional channels according to the original sub-feature maps of the target temporal frame on the convolutional channels and the original sub-feature maps of a next temporal frame adjacent to the target temporal frame on the convolutional channels; obtaining motion information feature maps of the target temporal frame on the convolutional channels by applying the motion information weights respectively on the original sub-feature maps of the target temporal frame on the convolutional channels; performing temporal convolution on the motion information feature maps to obtain temporal motion feature maps of the target temporal frame on the convolutional channels, wherein the performing temporal convolution on the motion information feature maps comprises determining to-be-convolved temporal frames according to the target temporal frame, wherein the to-be-convolved temporal frames are temporal frames adj acent to the target temporal frame, and convolving the motion information feature maps of the target temporal frame and motion information feature maps of the to-be-convolved temporal frames for each convolutional channel; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps. obtaining image data of a plurality of temporal frames in video data, and obtaining original sub-feature maps of the image data of each of the temporal frames on a plurality of convolutional channels by using a multi-channel convolutional layer; calculating, for each of the temporal frames as a target temporal frame, motion information weights respectively for the convolutional channels according to the original sub-feature maps of the target temporal frame on the convolutional channels and the original sub-feature maps of a next temporal frame adjacent to the target temporal frame on the convolutional channels; obtaining motion information feature maps of the target temporal frame on the convolutional channels by applying the motion information weights respectively on the original sub-feature maps of the target temporal frame on the convolutional channels; performing temporal convolution on the motion information feature maps to obtain temporal motion feature maps of the target temporal frame on the convolutional channels, wherein the performing temporal convolution on the motion information feature maps comprises determining to-be-convolved temporal frames according to the target temporal frame, wherein the to-be-convolved temporal frames are temporal frames adj acent to the target temporal frame, and convolving the motion information feature maps of the target temporal frame and motion information feature maps of the to-be-convolved temporal frames for each convolutional channel; and recognizing an action type of a moving object in image data of the target temporal frame according to the temporal motion feature maps.