Aiming at the problem that image information is easily blurred during image semantic generation, an image semantic generation model based on the combination of gated recurrent unit(GRU) and global interaction of image information is proposed. Processing and word vector mapping methods achieve model-driven image semantic generation. The experimental results show that the model can better solve the problems of data sparseness and skewness. The use of GUR units further reduces the scale of low model parameters, speeds up the algorithm's convergence speed, effectively suppresses model overfitting, and improves the richness, accuracy and logicality of image content.