02450nas a2200301   4500000000100000008004100001653001600042653001500058653002300073653001900096653003500115653002300150653003200173653001500205653001200220653002600232653003400258653003100292653002200323653002400345653003100369100001900400700001600419245011100435856015900546300001400705520142900719                                       d10aBlock-chain10aBlockchain10aComputation theory10aCross-platform10aCultural communication entropy10aDigital humanities10aDigital humanity technology10aEcosystems10aEntropy10aHistoric preservation10aIntangible cultural heritages10aIntelligent ecology theory10aModeling analyzes10aQuantitative models10aSpatio-temporal dimensions1 aQianjiang Wang1 aLixian Yang00aModel Analysis of Communication Entropy of Intangible Cultural Heritage within a Smart Ecosystem Framework  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-105010183518&doi=10.1109%2FISCAIT64916.2025.11010323&partnerID=40&md5=4a36d180c0353eb05384af0f82968113  a1658-16623 aBased on the Intelligent Ecology Theory and Digital Humanities Technology, this study proposes Cultural Communication Entropy (CCE), a quantitative model for evaluating intangible cultural heritage (ICH) dissemination. The framework integrates spatiotemporal dimensions-open systems, nonlinear interactions, and dynamic entropy-through three core indicators: Communication Efficiency Entropy (E\textlessinf\textgreatere\textless/inf\textgreater), Information Fidelity Entropy (E\textlessinf\textgreaterf\textless/inf\textgreater), and Interaction Quality Entropy (E\textlessinf\textgreateri\textless/inf\textgreater). Empirical analysis of 5,000 official and 1,000 control videos from Douyin revealed critical patterns: (1) Exponential decay of E\textlessinf\textgreatere\textless/inf\textgreater with a 32\% acceleration post- 72 hours (attributed to platform algorithms); (2) Official content exhibited higher entropy stability (Median E\textlessinf\textgreateri\textless/inf\textgreater=1.45 vs. 1.12, Cohen s d=1.05); (3) High-entropy videos outperformed low-entropy counterparts in cross-platform sharing (23\% vs. 12\%) and cultural depth (E\textlessinf\textgreaterculture\textless/inf\textgreater=0.736 vs. 0.412). The model integrates LSTM path prediction and blockchain-enhanced fidelity optimization, validated via cross-platform API synchronization (Bilibili, r = 0.79) and blockchain-encrypted data verification.