In recent years, the research on influence maximization (IM) for social networks has attracted extensive attention from the scientific community due to the emergence of major application issues, such as information dissemination on the Internet and the blocking of COVID-19’s transmission chain. IM aims to identify a set of optimal influence seed nodes that would maximize the influence of information dissemination according to the propagation model for a specific application issue. The existing IM algorithms mainly focus on unidirectional-link influence propagation models and simulate IM issues as issues of optimizing the selection of discrete influence seed node combinations. However, they have a high computational time complexity and cannot be applied to solve IM issues for signed networks with large-scale conflicting relationships. To solve the above problems, this study starts by building a positive-negative influence propagation model and an IM optimization model readily applicable to signed networks. Then, the issue of selecting discrete seed node combinations is transformed into one of continuous network weight optimization for easier optimization by introducing a deep Q network composed of neural networks to select seed node sets. Finally, this study devises an IM algorithm based on evolutionary deep reinforcement learning for signed networks (SEDRL-IM). SEDRL-IM views the individuals in the evolutionary algorithm as strategies and combines the gradient-free global search of the evolutionary algorithm with the local search characteristics of reinforcement learning. In this way, it achieves the effective search for the optimal solution to the weight optimization issue of the Deep Q Network and further obtains the set of optimal influence seed nodes. Experiments are conducted on the benchmark signed network and real-world social network datasets. The extensive results show that the proposed SEDRL-IM algorithm is superior to the classical benchmark algorithms in both the influence propagation range and the solution efficiency.