This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han Dynasty text "Yantie Lun" (Discourses on Salt and Iron) as a case study, HistoLens demonstrates its transformative potential for both historical research and education. HistoLens integrates machine learning technology (especially LLMs), including named entity recognition, dynamic knowledge graph construction, geo-temporal visualization, and deep ideological stance analysis. This paper showcase HistoLens's capability to disentangle and illuminate the intricate interplay of Confucian and Legalist thought within "Yantie Lun" across political, economic, military, and ethnic dimensions, offering a more granular and data-driven understanding. A key contribution is the development of a novel machine teaching paradigm where LLMs provide explainable analyses of Confucian and Legalist ideas, leveraging an LLM-assisted curated dataset, thereby democratizing access to complex historical interpretations. HistoLens not only offers unprecedented perspectives on texts like "Yantie Lun" but also presents a robust, transferable methodology and a suite of tools for in-depth, multi-faceted historical inquiry, fostering significant innovation in digital humanities and the pedagogy of history.