In　the　cervical　region　of　middle-aged　and　elderly　patients,　cervical　spondylotic　myelopathy　(CSM)　is　frequently　recognized　as　the　primary　factor　that　contributes　to　spinal　cord　dysfunction.　Numbness　and　gait　disturbance　are　the　main　clinical　manifestations　of　CSM,　which　exhibits　as　a　stiff　and　spastic　gait　in　comparison　with　that　of　healthy　controls　(HCs).　Because　it　is　difficult　to　screen　CSM　in　the　primary　stage　which　easily　leading　to　a　delay　in　medication,　the　identification　of　CSM　followed　by　treatment　is　urgent.　The　aim　of　this　study　is　to　develop　an　automated　classification　method　for　the　screening　of　CSM,　using　fifty-four　lower　extremity　kinematic　parameters　derived　from　three-dimensional　gait　analysis.　The　present　study　employs　a　deep　neural　network　(DNN)　model　to　automatically　extract　informative　features　from　raw　gait　kinematic　data.　Hierarchically　placed　layers　in　the　DNN　produce　deep　feature　maps　that　are　used　to　screen　CSM　using　multiple　shallow　classifiers.　The　proposed　method　is　evaluated　using　a　self-constructed　gait　database　of　patients　diagnosed　with　CSM　and　HCs,　both　groups　consisting　of　45　individuals　within　a　similar　age　range.　Experimental　results　reveal　that　the　combination　of　deep　features　and　shallow　classifiers　yields　remarkable　accuracy　rates　for　binary　classification　with　twofold,　tenfold,　and　leave-one-out　cross-validation　methods,　all　achieving　an　accuracy　of　99.44　%.　The　data　suggest　that　our　approach　is　efficient　in　detecting　the　early　onset　CSM　and　performs　better　than　other　cutting-edge　techniques.