Hierarchical Contrastive Neural Topic Modeling Based on Optimal Transport: A Scalable Framework for Temporal Semantics

        In today's data-driven society, dynamic topic models are widely used to reveal the evolutionary patterns of latent topics in document collections over time, which is of great significance for knowledge evolution analysis, hotspot discovery, and trend prediction. Existing neural topic models still have deficiencies in modeling document--topic alignment and hierarchical semantic structures, such as high dependence on pre-trained language models, high inference costs, and lack of explicit hierarchical structure modeling. To address these issues, this paper proposes a novel **Hierarchical Contrastive Neural Topic Model based on Optimal Transport** (HCNTM), which introduces Wasserstein distance constraints and hierarchical contrastive learning losses under a Bayesian variational framework to achieve fine-grained alignment of document--topic distributions and multi-granularity semantic decoupling. Specifically, the model first constructs continuous-time topic embedding trajectories through neural ordinary differential equations, then employs optimal transport mapping to eliminate semantic drift between adjacent time slices, and further designs a contrastive learning strategy with positive and negative samples obtained from hierarchical clustering to explicitly enhance the hierarchical relationships of topic--word and topic--topic. Large-scale experiments show that this method significantly outperforms nine representative models including HiCOT and EnCOT on multiple real datasets in terms of topic coherence, evolution smoothness, and prediction accuracy. The research results not only validate the effectiveness of combining optimal transport with contrastive learning, but also provide new theoretical and methodological support for dynamic semantic evolution modeling.