We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between research concepts -- by modelling how OpenAlex concept networks evolve over time. Using 59 semantic and topological features, a two-stage LightGBM model jointly predicts the formation and the future weight of concept pairs, adding a regression stage that quantifies expected intensity to prior link-existence forecasts. Relative to the state of the art, the approach improves accuracy and explainability at once: comparative validation across four technology and biomedical domains yields ROC-AUC in [0.954, 0.967] at all horizons without re-tuning, exceeding the roughly 0.90 of prior models, while every forecast rests on structural, auditable features rather than opaque embeddings. Classification performance is high (AUC about 0.95) and regression remains stable (RMSLE 0.45 to 0.6 over one to five years). Feature attribution shows that structural factors -- particularly Adamic-Adar similarity and degree-based Hadamard measures -- consistently drive accuracy, suggesting that breakthrough-relevant recombinations emerge in tightly connected sub-networks. Two expert-anchored cases, quantum annealing and AI-enabled quantum architectures, show the model surfacing technological convergence consistent with expert expectations. We then outline a three-layer decision architecture -- detection, expert translation, institutional integration -- that turns these forecasts into evidence-based research strategy and policy, anchored in open data and explainable features.

翻译：我们提出一种可解释的机器学习方法，通过建模OpenAlex概念网络随时间演化的过程，来预测科学突破的结构性前兆——研究概念之间关联的出现与强化。该方法利用59个语义和拓扑特征，采用两阶段LightGBM模型联合预测概念对的形成及其未来权重，在原有链接存在性预测基础上增加回归阶段以量化预期强度。与现有技术相比，该方法同时提升了准确性与可解释性：在四个技术与生物医学领域的对比验证中，无需重新调参即可在不同预测时间跨度上实现[0.954, 0.967]的ROC-AUC值，超越先前模型约0.90的水平；同时每项预测均基于可审计的结构化特征而非不透明的嵌入表示。分类性能优异（AUC约0.95），回归稳定性良好（一至五年RMSLE为0.45至0.6）。特征重要性分析表明，结构因子——特别是Adamic-Adar相似度和基于度的Hadamard度量——持续驱动模型精度，说明突破性技术重组往往出现于高度互联的子网络中。两个以专家知识为锚定的案例（量子退火与人工智能赋能的量子架构）显示，该模型能揭示符合专家预期的技术融合趋势。最后我们提出三层决策架构——检测、专家转化、制度整合——将上述预测转化为基于开放数据与可解释特征的循证研究策略与政策。