Background:　In　the　field　of　mobile　health,　portable　dynamic　electrocardiogram　(ECG)　monitoring　devices　often　have　a　limited　number　of　lead　electrodes　due　to　considerations,　such　as　portability　and　battery　life.　This　situation　leads　to　a　contradiction　between　the　demand　for　standard　12-lead　ECG　information　and　the　limited　number　of　leads　collected　by　portable　devices.　Methods:　This　study　introduces　a　composite　ECG　vector　reconstruction　network　architecture　based　on　convolutional　neural　network　(CNN)　combined　with　recurrent　neural　network　by　using　leads　I,　II,　and　V2.　This　network　is　designed　to　reconstruct　three-lead　ECG　signals　into　12-lead　ECG　signals.　A　1D　CNN　abstracts　and　extracts　features　from　the　spatial　domain　of　the　ECG　signals,　and　a　bidirectional　long　short-term　memory　network　analyzes　the　temporal　trends　in　the　signals.　Then,　the　ECG　signals　are　inputted　into　the　model　in　a　multilead,　singlechannel　manner.　Results:　Under　inter-patient　conditions,　the　mean　reconstructed　Root　mean　squared　error　(RMSE)　for　precordial　leads　V1,　V3,　V4,　V5,　and　V6　were　28.7,　17.3,　24.2,　36.5,　and　25.5　mu　V,　respectively.　The　mean　overall　RMSE　and　reconstructed　Correlation　coefficient　(CC)　were　26.44　mu　V　and　0.9562,　respectively.　Conclusion:　This　paper　presents　a　solution　and　innovative　approach　for　recovering　12-lead　ECG　information　when　only　three-lead　information　is　available.　After　supplementing　with　comprehensive　leads,　we　can　analyze　the　cardiac　health　status　more　comprehensively　across　12　dimensions.