The　irreversible　development　of　road　subbase　strain　under　long-term　loading　may　result　in　emergency　in　the　foundation　of　transportation　infrastructure　systems,　and　thus　the　corresponding　analysis　is　of　great　importance　to　the　safe　of　society　and　economy.　To　improve　the　resilience　of　transportation　infrastructure　systems,　the　analysis　of　subbase　strain,　aiming　for　an　accurate　and　reliable　prediction,　is　of　necessity　for　transportation　engineers.　Traditional　methods　mainly　include　mathematical　and　statistical　regression　analysis　solely　based　on　the　monitoring　stress/strain　data.　To　analyze　the　monitoring　data　more　comprehensively　and　more　accurately,　we　conducted　an　intelligent　analysis　of　subbase　strain　based　on　a　long-term　monitoring　and　deep　learning　approaches,　which　comprehensively　considers　the　environment　conditions　(temperature,　water　content,　pressure,　etc.),　the　mechanical　responses　of　other　structural　layers,　etc.　The　comprehensive　monitoring　system　was　installed　on　the　section　from　Nancun　to　Shiyingmen　of　108　National　Highway　in　Beijing　in　2012,　including　asphalt　strain　sensors,　embedded　three-dimensional　strain　sensors,　temperature　sensors,　osmotic　pressure　sensors,　and　others.　In　this　study,　the　long-term　road　monitoring　data　of　the　eight　years　from　2012　to　2020　was　used　for　analysis.　The　traditional　Random　Forest　(RF)　method　and　three　kinds　of　deep　learning　models,　including　Long-Short　Term　Memory　neural　network　(LSTM)　model,　Bidirectional　LSTM-Convolution　Neural　Network　(BiLSTM-CNN)　model　and　Temporal　Convolution　Network　(TCN)　model,　were　employed　to　analyze　the　development　of　subbase　strain.　Test　results　showed　that　deep　learning　methods　for　the　analysis　of　long-term　monitoring　data　is　better　than　that　uses　the　traditional　machine　learning　algorithm,　in　which　the　prediction　accuracy　of　RF　is　70.15%,　the　prediction　accuracy　of　LSTM　method　is　76.94%,　the　prediction　accuracy　of　TCN　is　90.46%,　and　the　predication　accuracy　of　BiLSTM-CNN　is　94.57%.　Finally,　sensitivity　analysis　of　the　TCN　model　has　been　performed　to　examine　the　impact　of　timesteps　on　evaluation　metrics.　The　study　could　serve　reference　to　predict　the　development　of　subbase　strain　in　engineering　projects.