The invention discloses a zero-sample image classification and recognition method based on a multi-part attention mechanism, and the method comprises the following steps: S1, training a multi-part convolution detector; s2, training a semantic feature extractor; s3, acquiring pictures of the training set, and processing the pictures by training an attention detector; s4, carrying out loss calculation; and S5, repeating the calculation of the steps S3 and S4, performing testing when the algorithm loss is lower than a preset value, and selecting the minimum distance as a category value. Accordingto the method, a semantic segmentation mode is adopted, firstly, semantic segmentation is carried out on the whole picture to obtain effective parts, unnecessary redundant information is screened out, and then feature extraction is carried out on the multiple parts; for different parts, an attention mechanism is put forward to act on the different parts for weighting, so that each sample has a different weighting mode, and therefore, for each sample, parts with high weights can be generated, and the parts can better distinguish the parts from other types.