Neutrosophic SDEs and Lyapunov Stability for Innovative Teaching Models: A Complete Analysis for Chinese Language & Literature in the Digital Era

We develop a neutrosophic stochastic framework to evaluate innovative teaching models in Chinese Language & Literature under digital education. The state of teaching quality is represented as a neutrosophic triplet 𝑋𝑡 = (𝑇𝑡,𝐼𝑡,𝐹𝑡) ∈ [0,1]3, encoding benefit (truth), ambiguity (indeterminacy), and shortfall (falsity) in the sense of neutrosophic functions and calculus . We propose a three-equation neutrosophic SDE-each component a mean-reverting Ornstein-Uhlenbeck (OU) process-whose parameters are calibrated from observed weekly quality series. A neutrosophic Lyapunov function proves exponential stability (in mean) of the model's deterministic counterpart and yields interpretable sufficient conditions linking pedagogy variability to stability margins. A complete case study on three digital teaching models is presented with step-by-step calculations, uncertainty quantification, and a scalar Neutrosophic Teaching Innovation Efficiency score. The methodology is transparent, reproducible, and extensible to other humanities disciplines.