Embedding professional knowledge is crucial during developing vertical domain large language models (LLMs). While rapid progress of LLM coexists with barriers to professional knowledge access. Such coexistence highlights the need for effective knowledge management during vertical domain LLM development, yet existing research seldom focuses on this. To solve this gap, we conduct three works: (1) Based on insights of knowledge structural representation, we propose a graph-driven knowledge stream method that models knowledge enabling activities as measurable flows; (2) a knowledge graph (KG)-anchored evaluation protocol is proposed to quantify graph coverage, constraint consistency, hallucination rate, and retention for evaluating knowledge enablement; (3) we integrate graph-anchored evaluation, LLM-graph interaction pipeline, federated learning (FL), and blockchain as graph-driven vertical domain LLM development framework (GVLDF). GVLDF enables knowledge aggregation and reuse, efficacy auditing, and continual optimization. In a foundation pit engineering case study (58,451 tokens fine-tuned vertical domain LLM), GVLDF improved graph coverage from 0.712 to 0.889, raised constraint consistency from 94.3% to 98.1%, and reduced hallucination rate from 0.220 to 0.063, while maintaining retention above 0.87. These findings show that structural anchoring systematically enhances reliability, compliance, and sustainability in vertical LLM development. Our research provides a reliable theory and solution for sustainable enablement and robust deployment of vertical domain LLM.