Genotype imputation can compensate for the missing due to technical limitations by estimating the sample regions that are not covered in gene sequencing data with imputation， but the existing deep learning-based imputation methods cannot effectively capture the linkage among complete sequence loci， resulting in low overall imputation accuracy and high dispersion of batch sequence imputation accuracy. Therefore， FCSA （Fusing Convolution and Self-Attention）， an imputation method that fuses convolution and self-attention mechanism， was proposed to address the above problems， and two fusion modules were used to form encoder and decoder to construct network model. In the encoder fusion module， a self-attention layer was used to obtain the correlation among complete sequence loci， and the local features were extracted through the convolutional layer after fusing the correlation to global loci. In the decoder fusion module， the local features of the encoded low-dimensional vector were reconstructed by convolution， and the complete sequence was modeled and fused by self-attention layer. The genetic data of multiple species of animals were used for model training， and the comparison and validation were carried out on Dog， Pig and Chicken datasets. The results show that compared to SCDA （Sparse Convolutional Denoising Autoencoders）， AGIC （Autoencoder Genome Imputation and Compression） and U-net， FCSA achieves the highest average imputation accuracy at 10%， 20% and 30% missing rate. Ablation experimental results also show that the design of the two fusion modules is effective in improving the accuracy of genotype imputation.