The　application　of　pose　assessment　on　rehabilitation　training　has　gradually　received　attention　in　recent　years.　However,　current　evaluation　indicators　of　these　methods　are　mostly　based　on　the　score　or　scoring　function　that　defined　by　users,　which　is　too　subjective　and　hard　to　be　used　by　patients　directly.　In　this　paper,　we　conceptualized　a　new　idea　for　pose　matching,　namely　pose-guided　matching　that　aims　at　providing　objective　and　accurate　score,　feedback　and　guidance　(i.e.　guided)　to　the　patients　when　the　pose　is　compared　to　the　standard　pose.　More　specifically,　we　proposed　a　pair-based　Siamese　Convolutional　Neural　Network　(SCNN)　abbreviated　ST-AMCNN　to　realize　the　idea　of　pose-guided　matching　on　the　eight-section　brocade　dataset　which　is　one　of　the　most　representative　traditional　rehabilitation　exercises　in　China.　We　simplified　the　multi-stages　pose　matching　by　merging　two　standalone　modules　(i.e.　alignment　and　matching　module)　into　a　one-stage　task.　Such　that,　only　one　loss　function　is　required　to　tune,　which　reduces　the　computational　complexity.　On　top　of　the　Spatial　Transformer　Networks　(STN)　employed　as　an　alignment　module,　we　proposed　a　new　Attention-based　Multi-Scale　Convolution　(AMC)　to　match　different　posture　parts　(i.e.　multi-scale).　Furthermore,　the　proposed　AMC　can　assign　more　weight　to　useful　pose　features　as　opposed　to　other　irrelevant　features　e.g.　background　features　for　performance　gain.　Finally,　Gradient-weighted　Class　Activation　Mapping　(Grad-CAM)　is　adopted　to　visualize　the　matching　result　for　the　learner.　Experimental　results　indicate　that　ST-AMCNN　achieves　a　competitive　performance　than　the　state-ofthe-art　models　and　can　provide　accurate　feedback　for　learners　on　rehabilitation　training.　Simultaneously,　the　proposed　method　is　also　deployed　in　client　software　for　testing.