标题： 拆解水晶球：使用 SAISE 框架从临时预测到原理性初创企业评估 显示英文标题

标题： Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework

摘要： 人工智能（AI）在初创企业评估中的整合代表了一次重大的技术转变，然而支撑这一转变的学术研究在方法论上仍显得支离破碎。 现有的研究通常采用随意的方法，导致研究成果在成功定义、理论特征和严格验证方面存在不一致。 这种碎片化严重限制了当前预测模型的可比性、可靠性和实际应用价值。 为解决这一关键差距，本文对57项实证研究进行了全面的系统文献综述。 我们通过系统地映射定义人工智能驱动的初创企业预测领域的特征、算法、数据来源和评估实践，剖析了当前最先进的状态。 我们的综合分析揭示了一个由核心矛盾定义的领域：对共同工具包——风险投资数据库和基于树的集成方法——有强烈的趋同，但在方法严谨性上却存在显著分歧。 我们识别出四个基础性弱点：对“成功”定义的碎片化，理论指导与数据驱动特征工程之间的分歧，常见与最佳实践模型验证之间的鸿沟，以及在数据伦理和可解释性方面的初步探索。 针对这些发现，我们的主要贡献是提出了系统的人工智能驱动初创企业评估（SAISE）框架。 这个新颖的五阶段指导路线旨在引导研究人员从随意的预测转向有原则的评估。 通过强制实施一种连贯的端到端方法，强调阶段感知的问题定义、理论指导的数据合成、有原则的特征工程、严格的验证和风险意识的解释，SAISE框架为在这个迅速成熟领域内进行更具可比性、稳健和实际相关的研究提供了新的标准。 显示英文摘要

摘要： The integration of Artificial Intelligence (AI) into startup evaluation represents a significant technological shift, yet the academic research underpinning this transition remains methodologically fragmented. Existing studies often employ ad-hoc approaches, leading to a body of work with inconsistent definitions of success, atheoretical features, and a lack of rigorous validation. This fragmentation severely limits the comparability, reliability, and practical utility of current predictive models. To address this critical gap, this paper presents a comprehensive systematic literature review of 57 empirical studies. We deconstruct the current state-of-the-art by systematically mapping the features, algorithms, data sources, and evaluation practices that define the AI-driven startup prediction landscape. Our synthesis reveals a field defined by a central paradox: a strong convergence on a common toolkit -- venture databases and tree-based ensembles -- but a stark divergence in methodological rigor. We identify four foundational weaknesses: a fragmented definition of "success," a divide between theory-informed and data-driven feature engineering, a chasm between common and best-practice model validation, and a nascent approach to data ethics and explainability. In response to these findings, our primary contribution is the proposal of the Systematic AI-driven Startup Evaluation (SAISE) Framework. This novel, five-stage prescriptive roadmap is designed to guide researchers from ad-hoc prediction toward principled evaluation. By mandating a coherent, end-to-end methodology that emphasizes stage-aware problem definition, theory-informed data synthesis, principled feature engineering, rigorous validation, and risk-aware interpretation, the SAISE framework provides a new standard for conducting more comparable, robust, and practically relevant research in this rapidly maturing domain