The distributed pressure recognition method based on sensing arrays is usually to characterize pressure information as an image. Then, features for classification are extracted. However, there are still two problems. The first is the limited density of sensing arrays which leads to low resolution of the formed pressure images. The second is the existence of elastic coupling in flexible sensing arrays, which results in blurred edges of the pressure images. In this article, a hybrid-order channel response symmetric bilinear convolutional neural network (HoSB-CNN) is proposed. Firstly, the channel attention response CNN is constructed for enhancing the representation of first-order features. Secondly, symmetric bilinear features are proposed to improve the sensitivity to edges. In addition, due to the structural symmetry of the symmetric bilinear features, only the triangular matrix is retained in the storage and transfer of the features, which could reduce the network complexity. Finally, a multi-order feature hybrid strategy is used to enhance the nonlinear fitting ability of the network. And a press-letter dataset is constructed by self-built data collection platform and 8 × 8 sensor array to evaluate the HoSB-CNN. Results show that the accuracy of the proposed method is 98. 11% .