Abstract: A multi-modal adaptive fusion deep clustering model based on an auto-encoder includes an encoder structure, a multi-modal adaptive fusion layer, a decoder structure and a deep embedding clustering layer. The encoder is configured to enable a dataset to be respectively subjected to three types of nonlinear mappings of the auto-encoder, a convolutional auto-encoder and a convolutional variational auto-encoder to obtain potential features, respectively. The multi-modal adaptive feature fusion layer is configured to fuse the potential features into a common subspace in an adaptive spatial feature fusion mode to obtain a fused feature. The decoder is configured to decode the fused feature by using a structure symmetrical to the encoder to obtain a decoded reconstructed dataset. The deep embedding clustering layer is configured to cluster the fused feature Z and obtain a final accuracy ACC by comparing a clustering result with a true label.