Social e-commerce is an emerging e-commerce mode in response to the upgrading of consumption, which has become an important engine for the development of the digital economy. Knowledge transfer and sharing play vital roles in improving the competitiveness and the sustainability of social e-commerce platform enterprises. However, academic research on knowledge transfer for the social e-commerce platform enterprise’s operation team remains deficient. To help social e-commerce platform enterprises to improve performance and better seek survival and sustainable development, this paper constructs a knowledge transfer model for the social e-commerce platform enterprise’s operation team, in the self-centered sustainable ecological business mode, from the relationship between intra-organizational operation knowledge transfer and cross-organizational knowledge sharing for value co-creation, and explores knowledge transfer behaviors from the perspective of complex network-based evolutionary game under strategy imitation preferences. Simulation results indicate that relationships among knowledge transfer cost, knowledge synergy benefit, cross-organizational value co-creation benefit rate, and reward and punishment, along with strategy imitation preferences, significantly impact knowledge transfer behaviors of the social e-commerce platform enterprise’s operation team. When all the members of the social e-commerce platform enterprise’s operation team prefer to imitate the knowledge transfer strategies of the operation members with smaller knowledge transfer costs, the operation team is more likely to show a high proportion adopting the transfer strategy, requiring low knowledge synergy coefficient, reward, punishment, and cross-organizational value co-creation benefit rate to achieve stable and sustainable knowledge transfer. Conversely, the operation team is more likely to show a low proportion adopting the transfer strategy, requiring high knowledge synergy coefficient, reward, punishment, and cross-organizational value co-creation benefit rate to achieve stable and sustainable knowledge transfer. This study has significance as a guide for social e-commerce platform enterprises in deploying the self-centered sustainable ecological business mode.

Device-to-device (D2D) technology enables direct communication between devices, which can effectively solve the problem of insufficient spectrum resources in 5G communication technology. Since the channels are shared among multiple D2D user pairs, it may lead to serious interference between D2D user pairs. In order to reduce interference, effectively increase network capacity, and improve wireless spectrum utilization, this paper proposed a distributed resource allocation algorithm with the joint of a deep Q network (DQN) and an unsupervised learning network. Firstly, a DQN algorithm was constructed to solve the channel allocation in the dynamic and unknown environment in a distributed manner. Then, a deep power control neural network with the unsupervised learning strategy was constructed to output an optimized channel power control scheme to maximize the spectrum transmit sum-rate through the corresponding constraint processing. As opposed to traditional centralized approaches that require the collection of instantaneous global network information, the algorithm proposed in this paper used each transmitter as a learning agent to make channel selection and power control through a small amount of state information collected locally. The simulation results showed that the proposed algorithm was more effective in increasing the convergence speed and maximizing the transmit sum-rate than other traditional centralized and distributed algorithms.