Objective In response to issues such as insufficient data feature representation and imbalanced sample classes in semi-supervised object detection a semi-supervised object detection method based on feature alignment and feature fusion was proposed. Methods In common semi-supervised object detection frameworks pseudo-labels are generated solely based on classification scores. However high-confidence predictions do not always fully guarantee accurate bbox positioning. In order to solve problems of inaccurate positioning and insufficient feature representation inspired by the FAM-3D algorithm in the Consistent Teacher considering that the optimal features for classification and positioning may be at different scales the T-head feature alignment head algorithm was introduced and the classification and positioning branches were successfully aligned in Unbiased Teacher V2. Additionally ASFF was introduced to suppress inconsistency by spatially filtering conflict information thereby improving the scale invariance of features and achieving spatial fusion of features. The internal inconsistencies within the feature pyramid were addressed by learning the connections between different feature maps. Results According to experimental results the improved algorithm demonstrated certain performance improvements on the COCO dataset and VOC dataset. Conclusion The proposed algorithm effectively alleviates issues of insufficient data representation and imbalanced sample classes while also enhancing algorithm accuracy.