A method for recognizing a key time point in a video, executed by a computer device, characterized in that, the method comprises: obtaining at least one video segment by processing each image frame in the video through an image classification model, the image classification model being a machine learning model obtained by training according to a first sample image frame, the first sample image frame being an image frame marked with a shot type; wherein each of the at least one video segment comprises at least two consecutive image frames in the video, and each of the at least one video segment corresponds to one shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by processing a target video segment through an image detection model, the image detection model being a machine learning model obtained by training according to a second sample image frame, the second sample image frame being an image frame marked with the location of the first object and the location of the second object; the target video segment being a video segment corresponding to a designated shot type in the at least one video segment; and determining, in a case that a preset condition is met between the location of the first object and the location of the second object in a first image frame, the key time point of the video according to a time point of the first image frame in the video; the first image frame being an image frame of the target video segment, wherein the determining, in a case that a preset condition is met between the location of the first object and the location of the second object in a first image frame, the key time point of the video according to a time point of the first image frame in the video comprises: determining, in a case that a distance between the location of the first object and the location of the second object in the first image frame is less than a distance threshold, the key time point of the video according to the time point of the first image frame in the video. obtaining at least one video segment by processing each image frame in the video through an image classification model, the image classification model being a machine learning model obtained by training according to a first sample image frame, the first sample image frame being an image frame marked with a shot type; wherein each of the at least one video segment comprises at least two consecutive image frames in the video, and each of the at least one video segment corresponds to one shot type; obtaining respective locations of a first object and a second object in an image frame of the target video segment by processing a target video segment through an image detection model, the image detection model being a machine learning model obtained by training according to a second sample image frame, the second sample image frame being an image frame marked with the location of the first object and the location of the second object; the target video segment being a video segment corresponding to a designated shot type in the at least one video segment; and determining, in a case that a preset condition is met between the location of the first object and the location of the second object in a first image frame, the key time point of the video according to a time point of the first image frame in the video; the first image frame being an image frame of the target video segment, wherein the determining, in a case that a preset condition is met between the location of the first object and the location of the second object in a first image frame, the key time point of the video according to a time point of the first image frame in the video comprises: determining, in a case that a distance between the location of the first object and the location of the second object in the first image frame is less than a distance threshold, the key time point of the video according to the time point of the first image frame in the video.