This research paper proposes a novel "Logic-of-Thought" (LoT) prompting method to improve the logical reasoning capabilities of Large Language Models (LLMs). Existing methods struggle with "unfaithful reasoning" and "information loss" during the translation of natural language into symbolic logic. LoT addresses these issues by extracting propositions and logical relations from the input text, extending them using logical reasoning laws, and translating the extended expressions back into natural language. This process enhances the LLM's reasoning ability by providing additional logical information within the prompt. The paper demonstrates the effectiveness of LoT by integrating it with various existing prompting methods, including Chain-of-Thought (CoT), Self-Consistency (SC), and Tree-of-Thoughts (ToT), and evaluating its performance on multiple logical reasoning datasets. The results highlight the significant improvements achieved by LoT across different tasks and prompting methods, demonstrating its potential to significantly advance the capabilities of LLMs in logical reasoning.