With　the　rapid　development　of　information　science　and　social　networks,　the　Internet　has　accumulated　various　data　containing　valuable　information　and　topics.　The　topic　model　has　become　one　of　the　primary　semantic　modeling　and　classification　methods.　It　has　been　widely　studied　in　academia　and　industry.　However,　most　topic　models　only　focus　on　long　texts　and　often　suffer　from　semantic　sparsity　problems.　The　sparse,　short　text　content　and　irregular　data　have　brought　major　challenges　to　the　application　of　topic　models　in　semantic　modeling　and　topic　discovery.　To　overcome　these　challenges,　researchers　have　explored　topic　models　and　achieved　excellent　results.　However,　most　of　the　current　topic　models　are　applicable　to　a　specific　model　task.　The　majority　of　current　reviews　ignore　the　whole-cycle　perspective　and　framework.　It　brings　great　challenges　for　novices　to　learn　topic　models.　To　deal　with　the　above　challenges,　we　investigate　more　than　a　hundred　papers　on　topic　models　and　summarize　the　research　progress　on　the　entire　topic　model　process,　including　theory,　method,　datasets,　and　evaluation　indicator.　In　addition,　we　also　analyzed　the　statistical　data　results　of　the　topic　model　through　experiments　and　introduced　its　applications　in　different　fields.　The　paper　provides　a　whole-cycle　learning　path　for　novices.　It　encourages　researchers　to　give　more　attention　to　the　topic　model　algorithm　and　the　theory　itself　without　paying　extra　attention　to　understanding　the　relevant　datasets,　evaluation　methods　and　latest　progress.