For　Internet　forum　Points　of　Interest　(PoI),　existing　analysis　methods　are　usually　lack　of　usability　analysis　under　different　conditions　and　ignore　the　long-term　variation,　which　lead　to　blindness　in　method　selection.　To　address　this　problem,　this　paper　proposed　a　PoI　variation　prediction　framework　based　on　similarity　analysis　between　long　and　short　windows.　Based　on　the　framework,　this　paper　presented　5　PoI　analysis　algorithms　which　can　be　categorized　into　2　types,　i.e.,　the　traditional　sequence　analysis　methods　such　as　autoregressive　integrated　moving　average　model　(ARIMA),　support　vector　regressor　(SVR),　and　the　deep　learning　methods　such　as　convolutional　neural　network　(CNN),　long-short　term　memory　network　(LSTM),　Transformer　(TRM).　Specifically,　this　paper　firstly　divides　observed　data　into　long　and　short　windows,　and　extracts　key　words　as　PoI　of　each　window.　Then,　the　PoI　similarities　between　long　and　short　windows　are　calculated　for　training　and　prediction.　Finally,　series　of　experiments　is　conducted　based　on　real　Internet　forum　datasets.　The　results　show　that,　all　the　5　algorithms　could　predict　PoI　variations　well,　which　indicate　effectiveness　of　the　proposed　framework.　When　the　length　of　long　window　is　small,　traditional　methods　perform　better,　and　SVR　is　the　best.　On　the　contrary,　the　deep　learning　methods　show　superiority,　and　LSTM　performs　best.　The　results　could　provide　beneficial　references　for　PoI　variation　analysis　and　prediction　algorithms　selection　under　different　parameter　configurations.