[目的/意义]针对当前突破性论文早期识别中特征维度单一、解释性不足等问题，构建可解释性机器学习方法提升识别精准度与逻辑透明度，为科研管理与创新布局提供方法论支撑。[方法/过程]首先，从突破性论文的内涵出发，在传统特征的基础上引入创新属性测度，利用大语言Prompt诱导获取知识创新向量，构建突破性论文多维特征体系；其次，基于形式概念分析理论构建信息表达系统，结合统计相关性分析及FCA属性约简算法筛选核心特征，采用多种机器学习分类器进行模型识别效果预测；最后，构建基于概念格与SHAP分析的双层解释框架，形成从筛选规则到预测验证的可视化解释链。[结果/结论]XGBoost模型的F1值达0.952，显著优于传统方法；双层解释体系明确了高创新属性与强知识关联的特征组合规则，量化了单特征对预测结果的贡献度。

[Purpose/significance] Aiming at the problems of single feature dimension and insufficient interpretability in the early identification of current breakthrough papers, this study constructs an interpretable machine learning meth⁃od to improve the identification accuracy and logical transparency, providing methodological support for scientific re⁃search management and innovation layout. [Method/process] Firstly, starting from the connotation of breakthrough pa⁃pers, on the basis of traditional features, innovation attribute measurement is introduced, knowledge innovation vectors are obtained by inducing large language Prompts, and a multi-dimensional feature system of breakthrough papers is constructed; secondly, an information expression system is built based on formal concept analysis (FCA), core features are screened by combining statistical correlation analysis and FCA attribute reduction algorithm, and various machine learning classifiers are used to predict the model identification effect; finally, a two-layer interpretation framework based on FCA concept lattice and SHAP analysis is constructed to form a visual interpretation chain from screening rules to prediction verification. [Result/conclusion] XGBoost model has an F1 value of 0.952 on multi-disciplinary da⁃tasets, which is significantly better than traditional methods; the two-layer interpretation system clarifies the feature combination rules of high innovation attributes and strong knowledge correlation, and quantifies the contribution of sin⁃gle features to the prediction results.