As　easy-to-search　semantic　information,　human　clothing　attributes　have　important　research　value　in　the　field　of　computer　vision.　Existing　attribute　recognition　methods　encounter　problems　such　as　interference　from　environmental　factors,　and　as　a　result　show　poor　clothing　positioning　accuracy.　To　address　these　problems,　a　human　attribute　recognition　method　based　on　human　pose　estimation　and　multiple-feature　fusion　is　proposed.　First,　some　retrieval　results　are　obtained　for　subsequent　attribute　recognition　through　appearance　feature　matching.　Then,　through　a　deep　SSD-based　human　pose　estimation　method,　the　foreground　area　belonging　to　the　human　in　the　image　is　located,　and　the　background　interference　is　excluded.　Finally,　the　analytical　results　of　various　methods　are　combined.　The　iterative　smoothing　process　and　the　maximum　posteriori　probability　assignment　method　are　adopted　to　enhance　the　correlation　between　attribute　labels　and　pixels,　and　the　final　attribute　recognition　results　are　obtained.　Experiments　on　the　benchmark　dataset　show　that　the　performance　of　our　model　is　improved,　and　solves　the　problems　of　inaccurate　clothing　label　recognition　and　pixel　resolution　area　deviation　in　a　single　recognition　mode.　©　2020,　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature.