A method for recognizing human body attributes, executable by an electronic device, the method comprising: acquiring a sample image of human body containing a plurality of areas to be detected, the areas to be detected being labeled with actual values of human body attributes; generating, through a recognition model, a global heat map of the sample image of human body and a local heat map of each area to be detected, wherein the global heat map is used for representing heat information of the human body in the sample image, and the local heat map is used for representing heat information of the human body in the area to be detected; fusing the global heat map and the local heat maps to generate a fused image, and performing human body attribute recognition on the fused image to obtain predicted values of human body attributes of the sample image of human body; characterized by, determining a focus area of each type of human body attribute according to the global heat map and the local heat maps, wherein the determining comprises: generating a class activation map corresponding to each type of human body attribute according to the global heat map and the local heat maps, wherein the class activation map is a heat map used for highlighting a specific area corresponding to the type of human body attribute; mirroring the global heat map and the local heat maps respectively to obtain a mirrored global heat map and mirrored local heat maps; generating a mirrored class activation map of each type of human body attribute based on the mirrored global heat map and the mirrored local heat maps; and determining the focus area of each type of human body attribute according to the class activation map and the mirrored class activation map; correcting the recognition model by using the focus area, the actual values of human body attributes, and the predicted values of human body attributes, wherein the recognition model is corrected based on a first loss function of the recognition model and a second loss function corresponding to the focus area, the first loss function is calculated based on the actual values of human body attributes and the predicted values of human body attributes, and the second loss function is acquired through the recognition model; and performing human body attribute recognition on an image to be recognized based on the corrected recognition model. acquiring a sample image of human body containing a plurality of areas to be detected, the areas to be detected being labeled with actual values of human body attributes; generating, through a recognition model, a global heat map of the sample image of human body and a local heat map of each area to be detected, wherein the global heat map is used for representing heat information of the human body in the sample image, and the local heat map is used for representing heat information of the human body in the area to be detected; fusing the global heat map and the local heat maps to generate a fused image, and performing human body attribute recognition on the fused image to obtain predicted values of human body attributes of the sample image of human body; characterized by, determining a focus area of each type of human body attribute according to the global heat map and the local heat maps, wherein the determining comprises: generating a class activation map corresponding to each type of human body attribute according to the global heat map and the local heat maps, wherein the class activation map is a heat map used for highlighting a specific area corresponding to the type of human body attribute; mirroring the global heat map and the local heat maps respectively to obtain a mirrored global heat map and mirrored local heat maps; generating a mirrored class activation map of each type of human body attribute based on the mirrored global heat map and the mirrored local heat maps; and determining the focus area of each type of human body attribute according to the class activation map and the mirrored class activation map; generating a class activation map corresponding to each type of human body attribute according to the global heat map and the local heat maps, wherein the class activation map is a heat map used for highlighting a specific area corresponding to the type of human body attribute; mirroring the global heat map and the local heat maps respectively to obtain a mirrored global heat map and mirrored local heat maps; generating a mirrored class activation map of each type of human body attribute based on the mirrored global heat map and the mirrored local heat maps; and determining the focus area of each type of human body attribute according to the class activation map and the mirrored class activation map; correcting the recognition model by using the focus area, the actual values of human body attributes, and the predicted values of human body attributes, wherein the recognition model is corrected based on a first loss function of the recognition model and a second loss function corresponding to the focus area, the first loss function is calculated based on the actual values of human body attributes and the predicted values of human body attributes, and the second loss function is acquired through the recognition model; and performing human body attribute recognition on an image to be recognized based on the corrected recognition model.