In the fields of computer vision and intelligent recognition, with the increasing complexity and dynamism of crane scenes, how to use digital twins technology and artificial intelligence technology to improve the accuracy and efficiency of crane image recognition has become a research hotspot. Based on the combination of digital twin technology, convolutional neural networks, and long short-term memory networks (DTE-CNN-LSTM), an algorithm has been developed to achieve intelligent recognition and semantic understanding of complex crane operation scenes by introducing a virtual simulation environment for image feature optimization and processing. The research results indicate that the DTE-CNN-LSTM algorithm performs well in multiple crane scenarios. After optimization, the recognition accuracy of night cranes reached 100%, the recognition accuracy of rainy scenes increased to 95%, and the misclassification rate decreased to 5%. The recognition accuracy of obstacle has reached 98%, and the recognition accuracy of crane operation area has reached 90%, significantly reducing the misclassification rate. Overall, the DTE-CNN-LSTM algorithm achieved an accuracy rate of 96.70% and an F1 value of 96.64% in classification tasks for different crane scenarios. Its robustness and generalization ability in complex crane working environments have been verified, demonstrating high potential for application.