This study proposes an intelligent function scheduling algorithm based on Deep Q-learning (DQN) to address challenges in serverless computing within multi-cloud environments. Using Kubernetes' multi-tenant mechanism, the algorithm enables seamless cross-platform interoperability. It optimizes resource utilization, network latency, and scheduling costs. Experiments show a 33% improvement in resource utilization, a 40% reduction in function response time, and a 30% decrease in call overhead compared to traditional static scheduling algorithms. While this approach has limitations, it provides a foundation for advancing multi-cloud serverless computing.