The　modeling　and　forecasting　of　BBS　(Bulletin　Board　System)　posts　time　series　is　crucial　for　government　agencies,　corporations　and　website　operators　to　monitor　public　opinion.　Accurate　prediction　of　the　number　of　BBS　posts　will　assist　government　agencies　or　corporations　in　making　timely　decisions　and　estimating　the　future　number　of　BBS　posts　will　help　website　operators　to　allocate　resources　to　deal　with　the　possible　hot　events　pressure.　By　combining　sample　entropy　(SampEn)　and　deep　neural　networks　(DNN),　an　approach　(SampEn-DNN)　is　proposed　for　BBS　posts　time　series　modeling　and　forecasting.　The　main　idea　of　SampEn-DNN　is　to　utilize　SampEn　to　decide　the　input　vectors　of　DNN　with　smallest　complexity,　and　DNN　to　enhance　the　prediction　performance　of　time　series.　Selecting　Tianya　Zatan　new　posts　as　the　data　source,　the　performances　of　SampEn-DNN　were　compared　with　auto-regressive　integrated　moving　average　(ARIMA),　seasonal　ARIMA,　polynomial　regression,　neural　networks,　etc.　approaches　for　prediction　of　the　daily　number　of　new　posts.　From　the　experimental　results,　it　can　be　found　that　the　proposed　approach　SampEn-DNN　outperforms　the　state-of-the-art　approaches　for　BBS　posts　time　series　modeling　and　forecasting.