In order to improve the target detection ability of CenterNet Ancor-free target detection network, an improved CenterNet target detection network based on attention feature fusion and multi-scale feature extraction network was proposed. Firstly, in order to improve the expression ability of the network for multi-scale targets, an adaptive multi-scale feature extraction network was designed. The feature map is resampled by cavity convolution to obtain multi-scale feature information, and the fusion was carried out on the spatial dimension. Secondly, in order to better integrate semantic and scale inconsistent features, a feature fusion module based on channel local attention was proposed. the fusion weight between shallow features and deep features was adaptively learned, and the key feature information of different perceptual domains was retained. Finally, the algorithm was verified on VOC 2007 test set. The experimental results showed that the detection accuracy of the final algorithm reaches 80. 94%, which was 3. 82% higher than the baseline algorithm CenterNet, and effectively improves the final performance of the Ancor-free target detection algorithm.