Combining　the　extracted　tongue　features　with　other　medical　indicators　can　effectively　judge　the　diseases　of　patients.　The　previous　work　usually　only　analyzes　a　certain　feature　of　the　tongue　body　and　is　unable　to　extract　multiple　features　simultaneously.　In　this　study,　a　multi-label　classification　network　named　TIM-Net　is　proposed,　which　integrates　global　and　local　features　to　achieve　multi-label　intelligent　diagnosis　of　Chinese　medicine　tongue　images.　First,　a　feature　extraction　network　based　on　ResNet　is　proposed　to　capture　the　features　of　tongue　images　more　sufficiently.　Then,　a　multi-label　classification　algorithm　fusing　global　and　local　features　is　proposed,　and　targeted　screening　operations　are　carried　out　on　the　class-related　feature　maps　based　on　global　confidence.　In　addition,　a　logical　masking　algorithm　is　proposed　to　ensure　that　the　local　features　can　only　correct　the　feature　labels　they　represent,　and　do　not　interfere　with　other　feature　labels.　The　classification　accuracy　is　further　improved　by　using　local　feature　confidence　and　correcting　the　global　classification　results.　Finally,　the　experimental　results　indicate　that　the　classification　accuracy　of　the　tongue　images　is　gradually　improved　through　optimizing　the　feature　extraction　network　and　fusing　local　features,　and　it　exceeds　other　state-of-the-art　multi-label　classification　networks.　©　2024　The　Authors.　IET　Image　Processing　published　by　John　Wiley　&　Sons　Ltd　on　behalf　of　The　Institution　of　Engineering　and　Technology.