Identifying influential spreaders in complex networks is crucial for applications in disease control, information dissemination, and social network analysis. The gravity model, a distinctive approach for identifying influential spreaders, has attracted significant attention due to its ability to integrate node influence and the distance between nodes. However, the law of gravity is symmetric, whereas the influence between different nodes is asymmetric. Existing gravity model-based methods commonly rely on the topological distance as a metric to measure the distance between nodes. Such reliance neglects the strength or frequency of connections between nodes, resulting in symmetric influence values between node pairs, which ultimately leads to an inaccurate assessment of node influence. Moreover, these methods often overlook cycle structures within networks, which provide redundant pathways for nodes and contribute significantly to the overall connectivity and stability of the network. In this paper, we propose a hybrid method called HGC, which integrates the gravity model with effective distance and incorporates cycle structure to address the issues above. Effective distance, derived from probabilities, measures the distance between a source node and others by considering its connectivity, providing a more accurate reflection of actual relationships between nodes. Experiments on eight real-world networks using the Susceptible-Infected-Recovered model show that HGC outperforms seven other methods in identifying influential nodes.