In order to overcome the difficulty of visual feature extraction and meet the real-time recognition demand of Beijing Opera characters, a Convolutional Neural Network based on HyperColumn Attention(HCA-CNN) was proposed to extract and recognize the fine-grained features of Beijing Opera characters. The idea of HyperColumn features used for image segmentation and fine-grained positioning were applied to the attention mechanism used for key area positioning in the network. The multi-layer superposition features was formed by concatenating the backbone classification network in the forms of pixel points through the HyperColumn set, so as to better take into account both the early shallow spatial features and the late depth category semantic features, and improve the accuracy of positioning task and backbone network classification task. At the same time, the lightweight MobileNetV2 was adopted as the backbone network of the network, which better met the real-time requirement of video application scenarios. In addition, the BeiJing Opera Role(BJOR) dataset was created and the ablation experiments were carried out on this dataset. Experimental results show that, compared with the traditional fine-grained Recurrent Attention Convolutional Neural Network(RA-CNN), HCA-CNN not only improves the accuracy index by 0.63 percentage points, but also reduces the Memory Usage and Params by 162.84 MB and 131.5 MB respectively, and reduces the times of multiplication and addition Mult-Adds and floating-point operations per second FLOPs by 39 885×10 ⁶ times and 51 886×10 ⁶ times respectively. It verifies that the proposed HCA-CNN can effectively improve the accuracy and efficiency of Beijing Opera character recognition, and can meet the requirements of practical applications.