Aiming at the problem of missed and false detections caused by the lack of semantic information in shallow feature layers and the lack of detailed information in high-level feature layers in the SSD (Single Shot Multibox Detector) object detection algorithm, an improved SSD object detection algorithm is proposed. First, the improved Comprehensive Convolutional Block Attention Module (CCBAM) is introduced to increase the sensitivity of the network to small targets. Then, a Hierarchical Feature Fusion Network (HFFNet) is constructed to fully integrate the detailed information from shallow layers and the semantic information from high-level layers. In addition, dilated convolutions are used during downsampling to extract features of different scales. In the upsampling process, pixel shuffling is used to increase the resolution of the high-level feature layers while reducing information loss, and then fused with the low -level feature layers to enhance the semantic information in the low-level feature layers. Finally, the Residual Feature Fusion Module (RFFM) is used to improve the integration of local and global information in the high-level feature layers and to enrich the feature information. The experiment demonstrated a 2.4% improvement over the original SSD algorithm, achieving a mAP@ 0.5 of 79.6% on the PASCAL VOC2007 test set at a recognition speed of 47.8 FPS.