Multi-Granularity Invariant Structure Learning for Text Classification in Entrepreneurship Policy

Data-driven text classification technology is crucial for understanding and managing a large number of entrepreneurial policy-related texts, yet it is hindered by two primary challenges. First, the intricate, multi-faceted nature of policy documents often leads to insufficient information extraction, as existing models struggle to synergistically leverage diverse information types, such as statistical regularities, linguistic structures, and external factual knowledge, resulting in semantic sparsity. Second, the performance of state-of-the-art deep learning models is heavily reliant on large-scale annotated data, a resource that is scarce and costly to acquire in entrepreneurial policy domains, rendering models susceptible to overfitting and poor generalization. To address these challenges, this paper proposes a Multi-granularity Invariant Structure Learning (MISL) model. Specifically, MISL first employs a multi-view feature engineering module that constructs and fuses distinct statistical, linguistic, and knowledge graphs to generate a comprehensive and rich semantic representation, thereby alleviating semantic sparsity. Furthermore, to enhance robustness and generalization from limited data, we introduce a dual invariant structure learning framework. This framework operates at two levels: (1) sample-invariant representation learning uses data augmentation and mutual information maximization to learn the essential semantic core of a text, invariant to superficial perturbations; (2) neighborhood-invariant semantic learning applies a contrastive objective on a nearest-neighbor graph to enforce intra-class compactness and inter-class separability in the feature space. Extensive experiments demonstrate that our proposed MISL model significantly outperforms state-of-the-art baselines, proving its effectiveness and robustness for classifying complex texts in entrepreneurial policy domains.