Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical and social transformation.Despite recent advances in diachronic semantics and discourse analysis, existing computational approaches often (i) concentrate on a single concept or a single corpus, making findings difficult to compare across heterogeneous sources, and (ii) remain confined to surface lexical evidence, offering insufficient computational and interpretive granularity when concepts are expressed implicitly.We propose HistLens, a unified, SAE-based framework for multi-concept, multi-corpus conceptualhistory analysis.The framework decomposes concept representations into interpretable features and tracks their activation dynamics over time and across sources, yielding comparable conceptual trajectories within a shared coordinate system.Experiments on long-span press corpora show that HistLens supports crossconcept, cross-corpus computation of patterns of idea evolution and enables implicit concept computation.By bridging conceptual modeling with interpretive needs, HistLens broadens the analytical perspectives and methodological repertoire available to social science and the humanities for diachronic text analysis.

The accuracy and reliability of continuous space curve estimation is the key to global exploration. An improved artificial intelligence algorithm is proposed for continuous space analysis. First, a wavelet basis ANN algorithm is proposed to determine the discretization strategy in continuous space. The hidden layer node transfer function in a BP neural network is replaced by a wavelet basis function, and the modified BP neural network is composed of a wavelet neural network. Second, an improved wolf algorithm is established. The core wolf system ensures the precision of the whole exploration task. Finally, main and auxiliary double cores and a five-class decision factor are used to establish a population classification model to solve the convergence of the algorithm.