Build failures are a major obstacle in RISC-V software migration, often involving complex interactions across logs, configurations, and environments. Traditional diagnostic tools struggle with the unstructured, multi-phase nature of build logs and lack semantic reasoning. We propose a two-stage framework for automated root cause analysis. RV-LAD compresses logs using template-based filtering and applies phase-aware anomaly detection via few-shot LLM prompting. MCTS-RCA integrates a domain-specific knowledge base with Monte Carlo Tree Search to perform LLM-guided multi-source reasoning under classification constraints. To support evaluation, we construct a curated dataset of 117 real-world RISC-V build failures, each annotated with logs, spec files, and repair records. Experiments show our approach achieves 75.2% diagnosis accuracy, surpassing prior LLM-based and rule-based methods. It also offers interpretable reasoning traces, enabling practical and transparent diagnosis. This work provides an effective and extensible solution for RCA in emerging software ecosystems like RISC-V, bridging large language models with domain-aware inference.