In magnetic resonance imaging (MRI) of living hearts, the edges of the left ventricular endocardium and myocardium are blurred due to movement, which results in inaccurate segmentation. To address this problem, we propose a left ventricular myocardium segmentation model OSFNet of 4D cardiac Cine-MRI based on the optical flow field and semantic feature fusion. The model includes the optical flow field calculation and semantic segmentation network, where the motion features calculated by the optical flow field are fused with the semantic features of images to achieve the optimal segmentation effect through network learning. The model employs the encoder-decoder architecture, and the proposed multi-receptive field module with average pooling is used to extract multi-scale semantic features and reduce feature losses. The decoder uses the multi-path up-sampling method and skip connections to ensure that semantic features are effectively restored. Then, the open dataset ACDC is applied to train and test the model, and the proposed model is compared with DenseNet and U-Net by the experiments of the left ventricular endocardium segmentation and the left ventricular endocardium and myocardium segmentation. Experimental results indicate that OSFNet achieves the best performance in several indicators such as Dice and HD.