Efficient resource scheduling in Computing Power Networks (CPN) is very important for improving system performance and reducing energy consumption, which are two key factors for the development of low-consumption computing technologies. To address the challenges posed by sparse rewards in the CPN, we propose CPN Resource Scheduling with Reward Shaping (C2RS), which is a novel scheduler developed using Deep Reinforcement Learning (DRL). By incorporating a customized reward shaping mechanism, C2RS infers a dense reward function that is closely consistent with the original sparse reward signal. This approach is able to learn optimal scheduling policies faster and significantly improves the efficiency of baseline methods. Our comprehensive evaluation in the simulated CPN environment shows that C2RS outperforms existing DRL-based schedulers on multiple performance metrics. Importantly, C2RS not only improves learning efficiency, but also leads to the development of more effective scheduling policies through its innovative reward shaping, thereby contributing to the advancement of energy-efficient computing solutions for big data analytics.