Texture is always one of the most important features of object images. Aiming at the low recognition accuracy of existingtexture recognition models in complex datasets,we propose a texture recognition algorithm based on improved residual pooling layer.Firstly,a multi-dimensional feature fusion module is proposed to?extract more effective texture features by using both high-level featuresand low-level features in this texture recognition model. Secondly,the residual pooling layer is improved. On the basis of the original residual pooling layer,the global maximum pooling branch is introduced to raise the global spatial structure observation for the texture recognition model. The feature vectors obtained from the original residual pooling layer and the global maximum pooling branch are splicedas texture features to improve the accuracy of texture recognition. Thirdly,with local binary patterns aided recognition strategy,localbinary patterns encoded mapping images are used to provide auxiliary information for the texture recognition model. Finally,the obtainedtexture features are input into the classification layer to obtain the texture recognition results. The proposed method has better texture recognition effect than that of the existing texture recognition methods B-CNN,Deep filter banks,Deep TEN,TEX-Net-LF,locality-awarecoding,DRP-Net.