## 2 Related Work
The authors in [18] proposes a new multi-modal knowledge graph based on the graph convolutional network to perceive depth graph attention network, which firstly constructs the multi-modal knowledge graph of remote sensing images, integrates various attributes and visual information of remote sensing images, and then carries out the information aggregation based on the depth relational attention, and enriches the node representations of remote sensing images by using the multi-modal information and the higher-order collaborative signals to significantly improve the accuracy of the remote sensing image recommendation. The above study has conducted in-depth research on users' potential interests, which has resulted in a significant improvement in the accuracy of remote sensing image recommendation. However, the above studies did not consider the real-time network environment of end users, and the time cost for users to acquire remote sensing images is still high, which cannot meet the real-time demand. In order to alleviate the time cost of acquiring remote sensing images, the authors in [21] combined the stream push technology and proposed a recommendation algorithm based on attention and multi-view fusion, constructs a value assessment function from multiple perspectives such as remote sensing users, data and services, defines a multi-view heuristic strategy to support resource discovery, and fuses these strategies through an attention network to realize the active and precise push of massive multi-source remote sensing resources. The authors in [22] proposed a remote sensing resource service recommendation model combining time-aware bidirectional long short-term memory (LSTM) neural network and similarity graph learning in combination with stream push technology. First, a sequence of interaction history behaviors is constructed based on the user's resource search history, and then, a graph structure of category similarity relationships between remote sensing resource categories is established based on the cosine similarity matrix, using LSTM to represent the history sequence, graph convolutional network to represent the graph structure, and combining the history sequence to construct the sequence of similarity relationships, and exploring the precise similarity relationships using the LSTM method. In [19], the authors proposes an efficient remote sensing image data retrieval and distribution scheme based on spatio-temporal features, which utilizes the spatio-temporal attributes of the remote sensing images as the markers of the content, introduces the in-orbit remote sensing image data segmentation algorithm according to the user's needs, and adopts multi-path transmission to solve the problems of limitation of the transmission resources, such as the capacity of the link and the length of the connection time, so as to realize the rapid retrieval and distribution of the in-orbit remote sensing image data. With the development of in-orbit processing technology and artificial intelligence technology, the future intelligent remote sensing satellite system will make the satellite in-orbit instant information push become a popular research direction in the field of remote sensing information recommendation. At that time, in addition to considering the characteristics of remote sensing data, it is inevitable that satellite topology changes, network performance, satellite service resources, user cache resources, user location, and other information need to be taken into account in order to realize the remote sensing data in-orbit proactive, real-time, intelligent push. In recent years, in order to improve the quality of service (QoS) for users in the ISTNs, there have been some studies on network resource optimization problems [23; 24; 25], but the active content distribution in ISTNs is still in the preliminary exploration stage. The problem is still in the preliminary exploration stage, mainly focusing on utilizing in-network caching techniques to improve the efficiency of content distribution in satellite networks.