Despite recent progress in features extraction using deep conventional auto-encoders， which have greatly benefited image clustering analysis with the satisfying clustering results on several simple image-datasets. However， the representation ability of the conventional auto-encoders is quite limit when they capture local features of complex and low-quality images. Therefore， a novel deep clustering method combining with visual features learning is proposed，i.e.， the convolutional auto-encoder with an asymmetric structure（ASCAE）， in which an asymmetric convolutional auto-encoder is used to learn feature representation， and then K-means algorithm performs clustering analysis for these learned features. To further improve the representation suitability for downstream clustering tasks， ASCAE method adopts strided convolution layers， and minimizes the reconstruction error of whole network regularized by L₂ error between the left and right fully connected layers. Experimental results on seven public image-datasets illustrate that the network of ASCAE method usually offers better feature representation and brings promising clustering performance presented by K-means algorithm. Clustering accuracy of ASCAE method is 0.754 and 0.918 on databases COIL-20 and MNIST， respectively， which is better than four depth clustering methods of the same type （AEC， IEC， DEC and DEN）.