Fine　particulate　matter　(PM2.5　)　poses　a　significant　threat　to　human　life　and　health,　and　therefore,　accurately　predicting　PM2.5　concentration　is　critical　for　controlling　air　pollution.　Two　improved　types　of　recurrent　neural　networks　(RNNs),　the　long　short-term　memory　(LSTM)　and　gated　recurrent　unit　(GRU),　have　been　widely　used　in　time　series　data　prediction　due　to　their　ability　to　capture　temporal　features.　However,　both　degrade　into　random　guessing　as　the　time　length　increases.　In　order　to　enhance　the　accuracy　of　PM2.5　concentration　prediction　and　address　the　issue　of　random　guessing　in　RNNs　neural　networks,　this　study　introduces　a　TCN-biGRU　neural　network　model.　This　model　is　a　hybrid　prediction　approach　based　on　combining　temporal　convolutional　networks　(TCN)　and　bidirectional　gated　recurrent　units　(bi-GRU).　TCN　extracts　higher-level　feature　information　from　longer　time　series　data　of　PM2.5　concentrations,　while　bi-GRU　captures　features　from　past　and　future　data　to　achieve　more　accurate　predictive　outcomes.　This　case　study　utilizes　data　from　monitoring　stations　in　Beijing　in　2021　for　conducting　PM2.5　prediction　experiments.　The　TCN-biGRU　model　achieves　an　average　absolute　error,　root　mean　square　error,　and　R-2　of　4.20,　7.71,　and　0.961　in　its　predictive　outcomes.　When　compared　to　the　predictive　outcomes　of　individual　LSTM,　GRU,　and　bi-GRU　models,　it　is　evident　that　the　TCN-biGRU　model　exhibits　smaller　errors　and　superior　predictive　performance.