Multi-label　classification　of　cell　clusters　is　crucial　for　thyroid　computer-aided　diagnosis.　The　intricate　spatial　configurations　and　multifaceted　semantic　annotations　inherent　in　thyroid　fine-needle　aspiration　biopsy　whole-slide　images　(FNAB-WSI)　pose　considerable　obstacles　to　the　precise　multi-label　classification　of　cell　clusters.　Considering　the　complex　spatial　structures　and　diverse　label　semantics　in　FNAB-WSI,　we　propose　a　multi-label　classification　method　of　cell　clusters　using　cellular　spatial-semantic　embedding.　This　method　effectively　processes　both　spatial　structure　and　multi-label　semantic　information.　To　address　the　challenge　posed　by　limited　training　data　for　hard-to-classify　categories,　our　method　partially　masks　easily　classifiable　cells　within　the　multi-label　clusters.　The　preprocessed　cell　cluster　images　are　then　fed　into　a　weighted　down-sampling　improved　Convolutional　vision　Transformer　(wCvT)　encoder　model　to　extract　spatial　features.　The　probability　scores　for　each　label　are　subsequently　obtained　through　a　multi-layer　Transformer　decoder　that　integrates　both　spatial　features　and　label　semantics,　thus　achieving　accurate　multi-label　classification　of　the　cell　clusters.　Experiments　conducted　on　a　self-built　FNAB-WSI　cell　cluster　dataset　demonstrate　an　optimal　classification　accuracy　of　90.26　%　mAP,　surpassing　the　highest　comparable　methods　by　4.96　%.　Moreover,　the　model　employs　a　minimal　number　of　parameters,　with　only　41.91　million　parameters,　achieving　a　tradeoff　between　accuracy　and　computational　efficiency.　This　means　that　the　proposed　method　could　be　utilized　as　a　swift　and　precise　computational　intelligence　aid　for　the　clinical　diagnosis　of　thyroid　cancer.　©　2024　Elsevier　B.V.