The invention relates to a human face recognition method based on a PCNN model, specifically a partitioning human face recognition method based on a weighted intensity PCNN model. The invention solves problems that a conventional human face recognition method based on the PCNN model is not fine in description of image features, carries out no-difference processing of the whole human face image through a group of parameters, and neglects the differences of all parts of a human face. On the basis of simplifying the PCNN model, the invention proposes the weighted intensity PCNN model, introduces the concepts of emission intensity of a spontaneous pulse, the emission intensity of a coupled pulse and the weighted intensity, and refines the output of the model. Meanwhile, the method employs the partitioning recognition during human face recognition. The method comprises the steps: enabling a human face image to be divided into blocks according to the difference of gray scale distribution of all parts of the human face image and the difference of local resolutions before recognition; adaptively setting the weight value of blocks according to the image blocks during recognition; finally enabling the recognition result of each block to be integrated in the recognition result of one human face image.