In recent years, RGB-D salient detection method has achieved better performance than RGB salient detection model by virtue of its rich geometric structure and spatial position information in depth maps and thus has been highly concerned by the academic community. However, the existing RGB-D detection model still faces the challenge of improving performance continuously. The emerging Transformer is good at modeling global information, while the convolutional neural network (CNN) is good at extracting local details. Therefore, effectively combining the advantages of CNN and Transformer to mine global and local information will help to improve the accuracy of salient object detection. For this purpose, an RGB-D salient object detection method based on cross-modal interactive fusion and global awareness is proposed in this study. The transformer network is embedded into U-Net to better extract features by combining the global attention mechanism with local convolution. First, with the help of the U-Net encoder-decoder structure, this study efficiently extracts multi-level complementary features and decodes them step by step to generate a salient feature map. Then, the Transformer module is used to learn the global dependency between high-level features to enhance the feature representation, and the progressive upsampling fusion strategy is used to process the input and reduce the introduction of noise information. Moreover, to reduce the negative impact of low-quality depth maps, the study also designs a cross-modal interactive fusion module to realize cross-modal feature fusion. Finally, experimental results on five benchmark datasets show that the proposed algorithm has an excellent performance than other latest algorithms.