## 1 Introduction  
At present, with the gradual deepening of space science exploration and the continuous development of space information technology, the construction of space information systems presents a state of explosive development. However, the construction of all kinds of spatial information systems is still separate, forming a situation of repeated construction and \"chimney-like development\". Various navigation, communication, remote-sensing, and other satellites occupy a large amount of orbital resources. When a single satellite system completes a given task, it will have more idle states, resulting in a waste of space resources [1]. The proposal of the space information network (SIN) provides a solution to the above problems. The SIN became a research hotspot in the global field [2]. The SIN is a network system that acquires, transmits, and processes spatial information in real time on various space platforms (such as synchronous satellites or mid-orbit satellites, stratospheric balloons, and manned or unmanned aerial vehicles) [3]. Compared with the ground network, the SIN plays an irreplaceable role in earth observation, emergency communication, air transportation, space TT&C, and the expansion of national strategic interests [4]. Compared with the traditional satellite network, the SIN has a series of characteristics such as complex structure, dynamic topology change, large cross-domain spatial scale, and so on. Therefore, we need to build an efficient SIN architecture to realize the effective allocation and management of multi-dimensional resources in the SIN, which is of great significance for the construction of the SIN [5]. Software-defined networking (SDN) is a new network architecture with data forwarding which is control-separated and software-programmable. SDN adopts a centralized control surface and a distributed forwarding surface. The control plane uses the developed control and forwarding communication interface to centralize the control of network devices on the forwarding plane, while providing flexible programmable capabilities [6]. The core idea of SDN is applied to the SIN. The data plane and control plane of satellite are separated, such that the satellite mainly implements simple forwarding and hardware configuration functions, thus solving the disadvantages of complex design and high cost of satellite nodes. The resources of transmission, caching, and computing of the whole network are allocated by the controller, which can not only lighten the burden of satellite nodes, but also benefit the unified management of the whole network [7; 8]. Deep reinforcement learning is a new research hotspot at present. It combines the perception ability of deep learning with the decision-making ability of reinforcement learning, and can realize direct control from original input to output. The Asynchronous Advantage Actor-Critic (A3C) algorithm is a deep reinforcement learning algorithm proposed by DeepMind in 2015 [9]. The A3C algorithm evaluates the output action. On the basis of using the Actor-Critic framework, the idea of asynchronous training is introduced, which effectively improves the training efficiency and reduces the training time [10]. The application of the A3C algorithm in the SIN can effectively solve the problem of dynamic allocation of the SIN resources, thereby improving the utilization efficiency of the SIN resources. Current research on the SIN mainly focuses on architecture design and the routing algorithm. Relevant research institutes and scholars proposed to apply SDN technology to the construction of the SIN, but there is a lack of specific multi-dimensional resource allocation methods. The main features of this paper are as follows:  
1. Based on the core idea of SDN, a hierarchical and domain-controlled SIN architecture is established. The overall network architecture and network control architecture are designed. 2. On the basis of the SDN-based SIN architecture, the transmission resources, caching resources, and computing resources in the SIN are unified.