Image description is to express the global information contained in the image in sentences. It requires that the image description generation model can extract image information and express the extracted image information in sentences. The traditional model is based on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), which can realize the function of image-to-sentence translation to a certain extent. However, this model has low accuracy and training speed when extracting key information of the image. To solve this problem, an improved attention mechanism image description generation model based on CNN and Long Short-Term Memory (LSTM) network was proposed. VGG19 and ResNet101 were used as the feature extraction networks, and group convolution was introduced into the attention mechanism to replace the traditional fully connected operation, so as to improve the evaluation indices.The model was trained by public datasets Flickr8K and Flickr30K and validated by various evaluation indices (BLEU(Bilingual Evaluation Understudy), ROUGE_L(Recall-Oriented Understudy for Gisting Evaluation), CIDEr(Consensus-based Image Description Evaluation), METEOR(Metric for Evaluation of Translation with Explicit Ordering)). Experimental results show that compared with the model with traditional attention mechanism, the proposed improved image description generation model with attention mechanism improves the accuracy of the image description task, and this model is better than the traditional model on all the four evaluation indices.