Traditional image captioning has the problems of the under-utilization of extracted image features, the lack of context information learning and too many training parameters. This study proposes an image captioning algorithm based on Vision-and-Language BERT (ViLBERT) and Bidirectional Long Short-Term Memory network (BiLSTM). The ViLBERT model is used as an encoder, which can combine image features and descriptive text information through the co-attention mechanism and output the joint feature vector of image and text. The decoder uses a BiLSTM combined with attention mechanism to generate image caption. The algorithm is trained and tested on MSCOCO2014, and the scores of evaluation criteria BLEU-4 and BLEU are 36.9 and 125.2 respectively. This indicates that the proposed algorithm is better than the image captioning based on the traditional image feature extraction combined with the attention mechanism. The comparison of generated text descriptions demonstrates that the image caption generated by this algorithm can describe the image information in more detail.