Intelligent Early Warning Model for Technological Paradigm Shift Risks in High-Tech Enterprises: An Integrated Framework of ISM–ANP-Entropy Method and Deep Autoencoder Network

Achieving accurate early warning of technological paradigm shift risks is crucial for high-tech enterprises to proactively manage strategic risks and seize opportunities for technological change. Based on a systematic identification of early warning factors for technological paradigm shift risks, this study constructs a chain-structured early warning model that integrates structural analysis, weight calculation, and intelligent algorithms. First, the Interpretive Structural Model (ISM) is used to analyze the hierarchical structure and dependencies among early warning factors, revealing the transmission path of risks from deep-rooted sources to surface-level phenomena. Second, the Analytic Network Process (ANP) and entropy method are integrated to synthesize subjective and objective information, calculating comprehensive weights for each factor and indicator while considering their mutual influences, thereby clarifying the priorities for risk management. Finally, addressing the nonlinear and small-sample characteristics of risk early warning, an intelligent early warning model based on a Deep Autoencoder Network (DAN) is constructed. Empirical testing on 75 high-tech enterprises shows that: ISM divides nine early warning factors into three levels with clear transmission relationships; ANP-entropy weights indicate that “technology assessment,” “enterprise competition,” and “innovation effort” are the core driving factors with the highest weights; and the DAN model, after training, achieves 93.33% accuracy in classifying technological paradigm risk levels on the test set, significantly outperforming traditional benchmarks such as One-Class SVM and Random Forest, demonstrating powerful nonlinear pattern recognition and adaptive assessment capabilities. This study provides methodological innovation and practical tools for achieving dynamic and intelligent early warning of technological paradigm risks.