With the continuous development of remote sensing technology， there has been a significant increase in the volume of remote sensing data. Providing accurate and timely remote sensing information recommendation services has become an urgent problem to solve. Existing remote sensing image recommendation algorithms mainly focus on user portrait， overlooking the influence of image content semantics on recommendation results. To address these issues， a remote sensing image recommendation method based on content interpretation was proposed. Firstly， an object extraction module based on YOLOv3 was used to extract objects from remote sensing images. Then， the location distribution vectors of key objects were integrated as image content information. Additionally， a multi-element user interest portrait was constructed and dynamically adjusted based on the user’s active search history to enhance the personality of recommendation results. Finally， the image content information was matched with the inherent attribute information of image and the user profile model to achieve accurate and intelligent recommendations of remote sensing data. Comparative experiments were conducted on real order data， to compare the proposed method with the newer recommendation method based solely on image attribute information. Experimental results show that the proposed method achieves a 70% improvement in the discrimination of positive and negative samples on the experimental data compared to the recommendation method considering user portrait. When using 10% training data with similar consumption time， the recommendation error rate decreases by 4.0 - 5.6 percentage points compared to the comparison method. When using 100% training data， the recommendation error rate decreases by 0.6 - 1.0 percentage points. These results validate the feasibility and effectiveness of the proposed method.