The traditional ultrasound Vector Flow Mapping （VFM） technology has the limitation that it requires the proprietary software to obtain raw Doppler and speckle tracking data. In order to solve the problem， a universal VFM method combined with deep learning was proposed. Firstly， the velocity scale was used to obtain the velocities along the acoustic beam direction provided by the color Doppler echocardiogram as the radial velocity components. Then， the U-Net model was used to automatically identify the contour of the left ventricular wall， the left ventricular wall velocities were calculated by the retrained CNNs for optical flow using Pyramid， Warping， and Cost volume （PWC-Net） model as the boundary condition of the continuity equation， and the velocity component of each blood particle perpendicular to the acoustic beam direction （that was the tangential velocity component） was obtained by solving the continuity equation. Finally， the velocity vector map of the heart flow field was synthesized， and the visualization of the streamline chart of the heart flow field was realized. Experimental results show that， the velocity vector map and streamline chart of the heart flow field obtained by the proposed method can accurately reflect the corresponding time phases of left ventricular， the obtained visualized results are consistent with the analysis results of the VFM workstation provided by Aloka， and conform to the characteristics of left ventricular fluid dynamics. As a universal and fast VFM method， the proposed method do not need any vendor’s technical support and proprietary software， and can further promote the application of VFM in clinical workflow.