Neural architecture search (NAS) is a core technique for automating AI model design, yet its efficiency is limited by the high cost of reliable architecture evaluation. To address this bottleneck, we introduce code-semantic transition modeling for NAS acceleration: before spending evaluator budget, we ask whether a proposal’s semantic change from its parent suggests a worthwhile architecture evolution. We instantiate this idea with ECO-SCE, an Evolutionary Code Semantic Change Estimator placed between the architecture generator and evaluator. Given a parent-to-proposal code transition, ECO-SCE filters likely low-gain proposals and reserves evaluator calls for proposals that are more likely to provide useful evolutionary progress. To train ECO-SCE, we construct NB101-SemPair from NAS-Bench-101 by sampling beneficial, harmful, and marginal transitions with diverse semantic and structural changes. ECO-SCE learns risk-aware transition utility from global semantic shifts, sparse semantic overlaps, and explicit structural edits, allowing it to skip likely low-gain proposals while sending promising or uncertain transitions to the evaluator. Across seven downstream benchmarks including CLRS, ECO-SCE reduces average GPU time by 70.88% and improves task performance in 6 out of 7 cases when inserted zero-shot into OpenEvolve.