Large language models (LLMs) perform well in complex reasoning tasks involving logical operations. However, the internal mechanisms underlying their logical reasoning capabilities remain insufficiently interpretable. Currently, it remains unclear whether neuron subsets closely associated with logical reasoning performance exist within LLMs, what forms of computational operations are performed by these neurons in the network, and how they influence model performance across different tasks. To investigate these key questions concerning the nature of LLM reasoning mechanisms, this study proposes an “identification-intervention-evaluation” framework aimed at systematically analyzing the neuronal operational basis and functional implementation of logical reasoning. A neuron localization method based on gradient importance is used to identify “logic neurons” that are highly sensitive to logical reasoning tasks, and targeted interventions are performed on their activation values to further evaluate their impact on model behavior. The results show that the identified logic-related neuron subsets are mainly concentrated in the middle layers of the network and predominantly located in feed-forward network (FFN) modules. Further analysis indicates that different logical tasks involve both shared neuron components and task-specific differences. Based on these findings, this study explores a lightweight targeted enhancement method for logical reasoning capabilities without modifying model weights. Experimental results show that this method improves the average accuracy of the model on long-chain reasoning tasks in LogicBench by 9.2% and improves performance on logic-sensitive general tasks by 4.57%.