This paper proposes an improved method based on YOLOv2 to solve the problems of low Chinese character detection rate, difficulty in small character detection and various character detection categories in natural scenes, and applies it to Chinese character detection in natural scenes. Firstly, k-means++ clustering algorithm is used to cluster the number of character target candidate box (anchor) and width-height ratio, increase the number of anchor, select 6 anchor with different sizes, and select candidate box more suitable for character detection.Then multi-layer feature fusion strategy is put forward. In the original network, outputing before the fourth maxpooling layer feature map after 3×3 and 1×1 size of convolution kernels for convolution operation and carry out 4 times under the sample to get the local feature, and outputing before the fifth maxpooling layer feature map after 3×3 and 1×1 size of convolution kernels for convolution operation and execute sampling of 2 times to get the local feature, to incorporate global features and local features, strengthen the network of local feature extraction, in order to improve the network of small target detection precision of the characters. At the same time, repeat convolution layer in high-level convolution is added, and the number of continuous and repeated 3×3×1024 convolution layers in high-level convolution is increased from 3 to 5, so as to improve the character detection category. Finally the use of Chinese Text in the Wild (CTW) data set of YOLOv2 and improved YOLOv2 algorithm contrast experiment, the experimental results show that the improved YOLOv2 algorithm measured in Chinese characters to the average precision mean (Mean Average Precision, mAP) was 78.3%, compared with the original YOLOv2 algorithm mAP value increased by 7.3%, and Chinese characters are significantly higher than other natural scene detection method.□□□□□□□□□□□□□□