This　study　presents　a　novel　approach　in　the　application　of　deep　learning　for　the　classification　of　esophageal　squamous　cell　carcinoma　(Escc)　using　whole-slide　images　(WSIs).　Our　methodology　uniquely　combines　Convolutional　Neural　Network　(CNN)　with　Transformer,　leveraging　the　strengths　of　both　architectures　to　enhance　the　accuracy　and　efficiency　of　cancer　detection　and　classification　in　histopathological　images.　In　this　research,　we　first　preprocess　a　substantial　dataset　of　WSI　samples,　annotated　by　expert　pathologists,　to　train　and　validate　our　model.　The　CNN　component　effectively　extracts　detailed　local　features　from　the　high-resolution　images,　while　the　Transformer,　known　for　its　capability　in　handling　sequential　data,　adeptly　manages　the　global　context,　addressing　the　challenges　posed　by　the　complex　and　heterogeneous　nature　of　WSIs.　The　accuracy,　F1　score,　recall,　and　precision　of　our　proposed　model　on　the　dataset　provided　by　Fujian　Cancer　Hospital　are　94.71%,　94.32%,　94.68%,　and　94.08%,　respectively,　which　are　significantly　better　than　other　models.　This　study　not　only　assists　pathologists　in　analyzing　esophageal　squamous　carcinoma　WSIs　but　also　paves　the　way　for　further　research　into　the　combined　application　of　CNN　and　Transformer　in　the　diagnosis　of　other　types　of　cancer.