As　an　important　entity　in　medical　texts,　time　information　plays　an　important　role　in　structuring　medical　information　and　supporting　clinical　decision-making.　In　this　paper,　time　expressions　in　Chinese　electronic　medical　record　text　of　depression　are　studied.　The　method　combines　regular　expressions　with　Conditional　random　fields　(CRFs)　to　recognize　time　expressions　in　Chinese　electronic　medical　records.　The　test　data　are　realistic　electronic　medical　records　of　depression　provided　by　a　hospital　in　Beijing.　The　proposed　method　uses　regular　expressions　to　initially　recognize　the　explicit　time　expression　in　the　text,　and　adds　a　dictionary　of　common　drugs　and　symptoms　of　depression　to　the　word　segmentation,　which　increases　the　accuracy　of　word　segmentation.　External　dictionary　features　are　optimized,　and　dictionaries　are　divided　into　time　modifier　dictionary,　time　representation　dictionary　and　event　dictionary,　which　effectively　improve　the　accuracy　and　recall　rate　of　conditional　random　field　recognition　results.　Experiments　show　that　the　accuracy　and　recall　rate　of　this　method　are　96.75%　and　93.33%　respectively.　©　2019,　Springer　Nature　Switzerland　AG.