In　recent　years,　computer　vision　applications　based　on　deep　learning　have　developed　rapidly,　and　systems　such　as　smart　buildings,　smart　cities　and　surveillance　and　security　have　become　key　research　areas　for　research　scholars.　Although　scholars　at　home　and　abroad　have　made　remarkable　achievements　in　the　field　of　multi-object　tracking,　in　complex　scenes　with　dense　targets　and　indistinguishable　target　backgrounds,　multi-object　tracking　algorithms　still　suffer　from　problems　such　as　target　miss　detection　and　false　detection,　resulting　in　a　large　number　of　disconnected,　erroneous　and　other　low-quality　target　tracking　trajectories.　To　address　the　above　problems,　this　paper　proposes　a　Multi-Object　Tracking　Algorithm　Based　on　Multi-layer　Feature　Adaptive　Fusion　(MFAF-MOT).　This　algorithm　designs　a　high-level　semantic　adaptive　weighting　module　and　effectively　achieve　multi-layer　feature　fusion.　By　adaptively　weighting　deep　high-level　semantic　features　and　shallow　fine-grained　features,　this　algorithm　improves　the　network＇s　ability　to　express　target　features　and　reduce　errors　such　as　target　miss　detection　and　false　detection.　The　algorithm　proposed　in　this　paper　achieves　competitive　results　in　three　different　scenarios,　reducing　problems　such　as　disconnected　target　trajectories　and　wrong　target　trajectories,　and　improving　the　precision　and　stability　of　the　multi-object　tracking　algorithm.　©　2022　IEEE.