This study utilizes Natural Language Processing (NLP) to analyze Charlotte Brontë's Jane Eyre, exploring how computational text analysis can make Victorian fiction more accessible to non-specialist readers. By employing an integrated framework that combines network text analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis, the research offers a multi-dimensional view of the novel's narrative structure. A detailed examination using TF-IDF and network centrality reveals that the discourse in Jane Eyre centers around Jane's narrated consciousness, particularly through verbs of perception, cognition, and speech that shape how events are perceived, evaluated, and socially negotiated. The LDA topic modeling identifies three key thematic clusters: cognitive-relational dynamics, domestic enclosure as a prerequisite for ethical action, and class-conscious interactions. Furthermore, sentiment analysis uncovers a predominantly neutral-to-negative emotional baseline, interrupted by positive peaks in romantic passages and specific spikes of fear during Gothic sequences. Collectively, these computational insights shed light on Jane's developmental journey through constraint, crisis, and self-determined resolution. The study concludes that computational evidence effectively enhances close reading by providing clear, replicable indicators of narrative structure, thus serving as valuable instructional tools for English language learners and enriching Digital Humanities pedagogy.