Traditional　Chinese　medicine　(TCM)　data　is　the　main　knowledge　resource　of　TCM,　which　contains　a　wealth　of　clinical　experience　knowledge.　Machine　learning　has　made　remarkable　achievements　in　natural　language　processing.　As　the　carrier　of　TCM　knowledge　and　information　stored　in　the　form　of　text,　using　machine　learning　method　to　study　these　TCM　data　can　save　a　lot　of　manpower　cost,　improve　the　objectivity　of　TCM,　promote　TCM　related　knowledge　better,　and　have　certain　guiding　significance　for　the　research　of　TCM　human　engineering　experiment.　This　paper　proposes　a　recommendation　algorithm　based　on　mutual　information　clustering.　Its　core　idea　is　calculating　mutual　information　between　two　symptoms,　and　set　symptom　＇relatives　and　friends　group＇,　after　getting　the　symptom　clustering　results　of　mutual　information,　then　combine　the　clustering　results　and　search　algorithm　to　achieve　the　effect　of　recommendation　and　filtering.　Experimental　results　show　that　the　proposed　method　is　effective.　©　2019　Published　under　licence　by　IOP　Publishing　Ltd.