| 陈劲,张可人,杨硕,朱子钦.面向新质生产力的未来技术预见模型探究[J].数量经济技术经济研究,2025,(9):5-28 | | 面向新质生产力的未来技术预见模型探究 | | Research on Future Technology Prediction Model for New Quality Productivity | | | | DOI： | | 中文关键词: 新质生产力 未来技术预见 人机混合智能 溯因推理 图神经网络 | | 英文关键词: New Quality Productivity Future Technology Prediction Human-computer Hybrid Intelligence Abductive Reasoning Graph Neural Network | | 基金项目: | | | 中文摘要: | | 全球科技创新和产业变革持续加速，新质生产力成为引领未来经济发展的关键动力。然而，新质生产力的未来技术预见单靠专家前瞻洞见存在有限理性、经验主义等痼疾，仅凭人工智能模型又存在幻觉、缺乏领域知识等问题。因此，本文提出一种融合机器智能与人类智慧的未来技术预见模型，将图神经网络方法视为机器层面的广义溯因推理路径，通过结构化学习重构“技术—产业—资金”间的复杂因果关联，同时引入专家排序评估与潜力判断，形成基于主观洞见的认知增强机制，实现了溯因推理和归纳演绎的有机结合。本文进一步选取七大关键产业领域，基于未来技术预见模型分析了新质生产力的未来技术趋势。研究发现，未来技术预见模型有效弥补了单一方法中存在的推理演绎不足或过拟合等缺陷，在复杂技术和产业估值预测中显著提升了效率和精度。七大关键产业领域的未来技术趋势矩阵呈现出动态演化与跨领域协同特征，各领域的未来技术发展展现出从单点突破向系统集成、从效率提升向范式革新的整体演进规律。本文的决策框架能够为技术趋势和产业机会的预测提供科学决策路径和量化评估工具，促进新质生产力的加快发展和现代化产业体系的建设。 | | 英文摘要: | | Under the accelerated background of global scientific and technological innovation and industrial change, new quality productivity (NQP) is a key driving force for future economic development. NQP is an advanced form of productive forces led by scientific and technological innovation, which aims at fostering new industries, new models, and new driving forces. Compared with traditional productive forces, NQP has diverted from its reliance on high-consumption production materials, capital expansion, and cheap labor. It has shifted to being innovation-driven, quality-centered, and promoting high-quality development through a significant increase in total factor productivity. This concept is highly consistent with China’s current historical process of advancing from high-speed growth to high-quality development, providing theoretical support that keeps pace with the times for the practice of Chinese-style modernization. Therefore, scientifically predicting and systematically planning the realization path of NQP and exploring its future technological trends in major industrial fields are urgent demands for promoting the deep integration of scientific and technological innovation and industrial innovation.Future technologies for NQP are a technological system centered on innovation, focusing on strategic frontier fields such as artificial intelligence, quantum computing, biomanufacturing, and advanced materials. Through the collaborative evolution and systematic integration of multiple technology clusters, it promotes the in-depth reconstruction of production factors toward digitalization, intelligence, and greenness. The emergence of such technologies is highly dependent on original and disruptive innovations. Their development is characterized by unpredictability, high volatility, and high risk, posing a huge challenge to future foresight. The current technological prediction mainly falls into two paths. The first one is machine intelligence based on information technology. Although this method can efficiently process large datasets and provide in-depth, comprehensive, and real-time analysis, it overly relies on historical data and lacks nonlinear and discontinuous reasoning and deduction capabilities. It has obvious deficiencies in providing forward-looking technological insights and innovative thinking. The second type is subjective insights based on expert opinions. Although subjective insights provide valuable perspectives for understanding future technological and market developments, they overly rely on expert experience, which leads to problems such as strong subjectivity, weak weighting, and insufficient adaptability, making it difficult to cope with the rapidly changing technological environment and overall market demands. In view of the limitations of the above two prediction methods, this study proposes a future technology prediction model that integrates machine intelligence and subjective insight to cope with the high uncertainty and complexity of future industries. The graph neural network method is regarded as the generalized abductive reasoning path at the machine level. The complex causal relationships among “technology-industry-capital” are reconstructed through structured learning. Further, expert ranking evaluation and potential judgment are introduced to form a cognitive enhancement mechanism based on subjective insights, thereby achieving the organic combination of abductive reasoning and inductive deduction.The future technology prediction model is a human-machine hybrid intelligent decision-making system that deeply integrates machine intelligence and subjective insights. By constructing a multi-source data network covering heterogeneous graphs of “technology-industry-capital,” and combining heterogeneous graph convolutional neural to dynamically mine the complex correlations among nodes, this model adopts a collaborative strategy of abductive reasoning and inductive deduction and supports the prediction of future technologies by analyzing historical causal chains and discovering machine learning patterns. A hybrid learning framework is designed to integrate unsupervised deep contrastive learning and supervised multi-task prediction, and the “ranking metric loss based on expert experience” function is introduced to transform the subjective insights of multi-level expert teams, such as strategic scientists, industry experts, and young scholars, into computable knowledge labels. Ultimately, a prediction system that integrates dynamic correlation modeling, multi-modal data fusion, and human-machine collaborative decision-making is formed, providing precise quantitative support for technological evolution and industrial transformation.This study further selected seven major industrial fields to analyze the future technological trends of NQP. It finds that the future technology prediction model effectively compensates for deficiencies, such as insufficient reasoning and deduction or overfitting existing in a single method, and significantly improves the efficiency and accuracy in the valuation prediction of complex technologies and industries. The future technological trend matrix of NQP formed for the seven key areas indicates dynamic evolution and cross-domain collaboration characteristics throughout the entire life cycle. The future technological development in each area demonstrates an overall evolution pattern from single-point breakthroughs to system integration and from efficiency improvement to paradigm innovation. The decision-making framework in this study can fully explore the potential of NQP, provide scientific decision-making paths and quantitative evaluation tools for predicting technological trends and industrial opportunities, and promote the accelerated development of NQP and the construction of a modern industrial system. | | 查看全文 相关附件： 下载数据代码附录 |
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