Objective Aiming at the issue of inadequate characterization capacity of CenterNet anchor-free object recognition algorithm a method based on context enhancement and feature fusion was proposed. Methods This method adopted the concepts of multi-receptive field and information fusion to construct an adaptive context extraction module and feature fusion strategy. Firstly the network obtained the contextual features of the target through the multipath dilated convolution of the adaptive context extraction module prompting deep networks to learn multi-scale information. Then nonlinear factors were added to the network through the ACON-C activation function adaptively activating the neurons of the network and enhancing the data-fitting ability of the network. Finally a joint attention feature fusion strategy was used to merge feature information at different levels. By integrating semantic features from the high-level network and positional features from the low-level network the feature information that is useful for the recognition task was captured. At the same time the correlation between the feature maps in multiple levels of channels was learned to enhance the network?? s focus on key target features. Results The proposed method achieved an mAP of 83. 82% on the PASCAL VOC public dataset an improvement of 3. 72% compared with the CenterNet baseline algorithm. It also outperformed classic algorithms such as Faster R-CNN SSD and YOLOv3 by 7. 4% 9. 5% and 3. 5% respectively. Conclusion The proposed method effectively enhances the detection performance of the CenterNet algorithm and the improved CenterNet has higher recognition accuracy compared with other target recognition algorithms. The proposed method proves to be practical in target detection applications validating the effectiveness of the proposed approach.