Aiming　at　the　poor　effect　of　deep　learning　algorithm　on　small　objects　detection,　the　SSD　object　detection　method　based　on　feature　fusion　is　proposed.　The　reasons　for　low　detection　rate　and　poor　robustness　of　classical　SSD　object　detection　methods　are　analysed;　and　through　the　theoretical　analysis　and　comparative　experiments,　the　characteristic　fusion　layer　was　proposed.　The　shallow　layers　with　high　resolution　and　deep　layers　with　strong　semantics　are　fused　with　the　feature　fusion　structure;　finally,　a　complete　feature　fusion　structure　is　designed　with　the　residual　block　to　increase　the　width　and　depth　of　the　network.　The　contrast　experiment　on　the　PASCAL　VOC　dataset　was　conducted　for　detection　capability　and　detection　accuracy,　and　experimental　result　indicates　that　when　the　confidence　is　set　to　0.5,　the　mAP　of　the　SSD　method　based　on　feature　fusion　is　78.04%,　which　is　0.8%　higher　than　the　classical　SSD　algorithm　and　4.8%　higher　than　the　Faster　RCNN　algorithm.　Obviously,　the　proposed　algorithm　improves　the　ability　of　small　objects,　and　verifies　the　effectiveness　of　the　proposed　algorithm.