Early　accurate　diagnosis　of　patellofemoral　pain　syndrome　(PFPS)　is　important　to　prevent　the　further　development　of　the　disease.　However,　traditional　diagnostic　methods　for　PFPS　mostly　rely　on　the　subjective　experience　of　doctors　and　subjective　feelings　of　the　patient,　which　do　not　have　an　accurate-unified　standard,　and　the　clinical　accuracy　is　not　high.　With　the　development　of　artificial　intelligence　technology,　artificial　neural　networks　are　increasingly　applied　in　medical　treatment　to　assist　doctors　in　diagnosis,　but　selecting　a　suitable　neural　network　model　must　be　considered.　In　this　paper,　an　intelligent　diagnostic　method　for　PFPS　was　proposed　on　the　basis　of　a　one-dimensional　convolutional　neural　network　(1D　CNN),　which　used　surface　electromyography　(sEMG)　signals　and　lower　limb　joint　angles　as　inputs,　and　discussed　the　model　from　three　aspects,　namely,　accuracy,　interpretability,　and　practicability.　This　article　utilized　the　running　and　walking　data　of　41　subjects　at　their　selected　speed,　including　26　PFPS　patients　(16　females　and　10　males)　and　16　painless　controls　(8　females　and　7　males).　In　the　proposed　method,　the　knee　flexion　angle,　hip　flexion　angle,　ankle　dorsiflexion　angle,　and　sEMG　signals　of　the　seven　muscles　around　the　knee　of　three　different　data　sets　(walking　data　set,　running　data　set,　and　walking　and　running　mixed　data　set)　were　used　as　input　of　the　1D　CNN.　Focal　loss　function　was　introduced　to　the　network　to　solve　the　problem　of　imbalance　between　positive　and　negative　samples　in　the　data　set　and　make　the　network　focus　on　learning　the　difficult-to-predict　samples.　Meanwhile,　the　attention　mechanism　was　added　to　the　network　to　observe　the　dimension　feature　that　the　network　pays　more　attention　to,　thereby　increasing　the　interpretability　of　the　model.　Finally,　the　depth　features　extracted　by　1D　CNN　were　combined　with　the　traditional　gender　features　to　improve　the　accuracy　of　the　model.　After　verification,　the　1D　CNN　had　the　best　performance　on　the　running　data　set　(accuracy　=　92.4%,　sensitivity　=　97%,　specificity　=　84%).　Compared　with　other　methods,　this　method　could　provide　new　ideas　for　the　development　of　models　that　assisted　doctors　in　diagnosing　PFPS　without　using　complex　biomechanical　modeling　and　with　high　objective　accuracy.