A construction scene image caption method based on attention mechanism and encoding-decoding architecture was proposed, in order to realize the image caption in the complex construction scenes such as poor light, night construction, long-distance dense small targets and so on. Convolutional neural network was used to construct encoder to extract rich visual features in construction images. Long short-term memory network was used to construct decoder to capture semantic features of words in sentences and learn mapping relationship between image features and semantic features of words. Attention mechanism was introduced to focus on significant features, suppress non-significant features and reduce interference of noise information. An image caption data set containing ten common construction scenes was constructed in order to verify the effectiveness of the proposed method. Experimental results show that the proposed method achieves high accuracy, has good image caption performance in complex construction scenes such as poor light, night construction, long-distance dense small targets and so on, and has strong generalization and adaptability.