基于学科交叉的未来产业识别研究

doi:10.13304/j.nykjdb.2024.0418

摘要/Abstract

摘要：

未来产业由前沿技术驱动，是把握未来发展主动权的关键，与学科交叉一样具有前沿科技、交叉融合和战略性特征，因此，基于学科交叉开展未来产业识别具有可行性。生物育种作为学科交叉的前沿领域，关系到国家粮食安全和人类生命健康，其未来产业空间巨大，围绕生物育种领域开展未来产业识别研究具有重要意义。根据技术推动模型，技术驱动始于基础研究，一般采用期刊文献表征基础研究情况，因此将期刊数据作为数据源。首先采用学科多样性综合指标测度筛选高学科交叉期刊文献，然后利用隐含狄利克雷分布（latent dirichlet allocation，LDA）模型对3个连续时间段文献进行主题分析获得技术群，最后通过主题变化趋势识别未来产业，并通过对技术群的细分领域和合作机构分析发现未来产业应用场景。通过该方法成功识别出合成生物、生物信息、生物与环境3个生物育种领域未来产业技术群，识别结果与业界保持一致，说明方法的可行性。并对生物育种领域未来产业技术群的细分领域和机构情况进行应用场景分析，以期为政府及相关科研机构布局未来产业提供决策支持。

关键词: 学科交叉, 未来产业, 主题模型, 生物育种

Abstract:

The future industry is driven by cutting-edge technology， and it is the key to grasp the initiative of future development. It has characteristics of cutting-edge technology， cross fusion and strategic， which is same as interdisciplinary studies， so it is feasible to carry out future industry identification based on interdisciplinary studies. As an frontier interdisciplinary field related to national food security and human life and health， the field of bio-breeding has a huge industrial space in the future. It is of great significance to study on the identification of future industries in the field of bio-breeding based on interdisciplinary. According to the technology push model， the technology drive begins with basic research， which is generally characterized by the use of journal literature， hence the use of journal data as a data source. Firstly， highly interdisciplinary literature was screened using the comprehensive indicator measure of disciplinary diversity， then the technology clusters were obtained by theme mining of three consecutive time-periods of literature using the latent dirichlet allocation （LDA） model， and finally identifying the future industry of bio-breeding through the trend of theme change， and discovering the future industrial application scenarios through the analyses of the sub-divisions of the technology clusters and the cooperating organizations. 3 future industrial technology clusters in field of bio-breeding： synthetic biology， bioinformatics， and biology and environment were successfully identified. The identification results were consistent with the industry， indicating the feasibility of the method. The application scenarios of the subfields and institutions of the future industrial technology clusters in field of bio-breeding were further analyzed， which provided decision support for the layout of future industries in the field of bio-breeding by government and laboratory.

Key words: interdisciplinarity, future industries, topic model, biological breeding

中图分类号:

S336

史敏, 徐珍艳, 罗建. 基于学科交叉的未来产业识别研究[J]. 中国农业科技导报, 2025, 27(7): 10-19.

Min SHI, Zhenyan XU, Jian LUO. Study on Identification of Future Industries Based on Interdisciplinary[J]. Journal of Agricultural Science and Technology, 2025, 27(7): 10-19.

图/表 5

参考文献 30

[1]	新华社. 习近平总书记首次提到“新质生产力”[EB/OL]. (2023-09-10) [2024-04-26]. .
[2]	黄群慧. 强化助力中国式现代化使命担当把握建设现代新型国有企业的着力点[J]. 国资报告, 2023(7): 16-17.
[3]	中国社会科学院工业经济研究所课题组. 世界主要经济体未来产业的战略布局[J]. 新经济导刊, 2023(2): 73-86.
[4]	中国政府网. 工业和信息化部等七部门关于推动未来产业创新发展的实施意见:工信部联科〔2024〕12号[EB/OL]. (2024-01-18) [2024-04-26]. .
[5]	张雪, 张志强. 学科交叉研究系统综述[J]. 图书情报工作, 2020, 64(14): 112-125.
	ZHANG X, ZHANG Z Q. Review on interdisciplinary research [J]. Libr. Inf. Serv., 2020, 64(14): 112-125.
[6]	路甬祥. 学科交叉与交叉科学的意义[J]. 中国科学院院刊, 2005, 20(1): 58-60.
	LU Y X. Implications in the disciplinary cross-breeding and inter-disciplinary sciences [J]. Bull. Chin. Acad. Sci., 2005, 20(1): 58-60.
[7]	新华社. 中央经济工作会议举行习近平李克强作重要讲话[EB/OL]. (2020-12-18) [2024-04-26]. .
[8]	新闻联播. 习近平主持召开中央全面深化改革委员会第二十次会议强调统筹指导构建新发展格局推进种业振兴推动青藏高原生态环境保护和可持续发展[EB/OL]. (2021-07-09) [2024-04-26]. .
[9]	余东华. “十四五”期间我国未来产业的培育与发展研究[J]. 天津社会科学, 2020, 3(3): 12-22.
[10]	王小林, 谢妮芸. 未来产业: 内涵特征、组织变革与生态建构[J]. 社会科学辑刊, 2023(6): 173-182.
[11]	SIGOV A, RATKIN L, IVANOV L A, et al.. Emerging enabling technologies for industry 4. 0 and beyond [J]. Inf. Syst. Front., 2024, 26(5): 1585-1595.
[12]	沈华, 王晓明, 潘教峰. 我国发展未来产业的机遇、挑战与对策建议[J]. 中国科学院院刊, 2021, 36(5): 565-572.
	SHEN H, WANG X M, PAN J F. Opportunities, challenges, and recommendations for development of future industries in China [J]. Bull. Chin. Acad. Sci., 2021, 36(5): 565-572.
[13]	刘仲林. 交叉科学时代的交叉研究[J]. 科学学研究, 1993, 11(2): 9-16.
	LIU Z L. Interdisciplinary research in the New Era of interdisciplinary science [J]. Stud. Sci. Sci., 1993, 11(2): 9-16.
[14]	张琳, 孙蓓蓓, 王贤文, 等. 交叉科学成果影响力研究: 使用数据与引用数据视角[J]. 情报学报, 2020, 39(5): 469-477.
	ZHANG L, SUN B B, WANG X W, et al.. The impact of interdisciplinarity: distinct effects on usage and citation [J]. J. China Soc. Sci. Tech. Inf., 2020, 39(5): 469-477.
[15]	CASSI L, MESCHEBA W, DE TURCKHEIM É. How to evaluate the degree of interdisciplinarity of an institution? [J]. Scientometrics, 2014, 101(3): 1871-1895.
[16]	李佳蕾, 安培浚, 肖仙桃. 学科交叉主题识别方法研究综述[J]. 数据分析与知识发现, 2023, 7(4): 1-15.
	LI J L, AN P J, XIAO X T. Review of methods for interdisciplinary topic identification [J]. Data Anal. Knowl. Discov., 2023, 7(4): 1-15.
[17]	PORTER A L, COHEN A S, DAVID ROESSNER J, et al.. Measuring researcher interdisciplinarity [J]. Scientometrics, 2007, 72(1): 117-147.
[18]	索传军, 肖玥. 学科交叉研究的现存问题与未来展望[J]. 图书情报工作, 2023, 67(1): 144-152.
	SUO C J, XIAO Y. The implementation dilemmas and perspectives of interdisciplinary research [J]. Libr. Inf. Serv., 2023, 67(1): 144-152.
[19]	范少萍, 安新颖, 晏归来, 等. 医学领域前沿主题识别方法研究[J]. 情报学报, 2018, 37(7): 686-694.
	FAN S P, AN X Y, YAN G L, et al.. Study on the recognition method of frontier topic in the medical field [J]. J. China Soc. Sci. Tech. Inf., 2018, 37(7): 686-694.
[20]	韩正琪, 刘小平, 徐涵. 基于Rao-stirling指数的学科交叉文献发现: 以纳米科学与纳米技术为例[J]. 图书情报工作, 2018, 62(1): 125-131.
	HAN Z Q, LIU X P, XU H. Interdisciplinary literature discovery based on Rao-stirling diversity indices: case studies in nanoscience and nanotechnology [J]. Libr. Inf. Serv., 2018, 62(1): 125-131.
[21]	张琳, 孙蓓蓓, 黄颖. 跨学科合作模式下的交叉科学测度研究——以ESI社会科学领域高被引学者为例[J]. 情报学报, 2018, 37(3): 231-242.
	ZHANG L, SUN B B, HUANG Y. Interdisciplinarity measurement based on interdisciplinary collaborations: a case study on highly cited researchers of ESI social sciences [J]. J. China Soc. Sci. Tech. Inf., 2018, 37(3): 231-242.
[22]	MOHR J W, BOGDANOV P. Introduction: topic models: what they are and why they matter [J]. Poetics, 2013, 41(6): 545-569.
[23]	刘冬东. 新兴交叉主题识别与引文扩散影响研究——以计算生物学领域为例[D]. 郑州: 华北水利水电大学, 2020.
	LIU D D. The identification and citation diffusion impact of the emerging interdisciplinary topics: a case study of computational biology [D]. Zhengzhou: North China University of Water Resources and Electric Power, 2020.
[24]	熊回香, 窦燕. 基于LDA主题模型的标签混合推荐研究[J]. 图书情报工作, 2018, 62(3): 104-113.
	XIONG H X, DOU Y. Research on tag hybrid recommendation based on LDA topic model [J]. Libr. Inf. Serv., 2018, 62(3): 104-113.
[25]	肖国樱. 加快推进湖南省生物育种研究和产业化应用的建议[J]. 杂交水稻, 2022, 37(3): 1-6.
	XIAO G Y. Suggestions on accelerating the research and application of bioengineered breeding in Hunan province [J]. Hybrid Rice, 2022, 37(3): 1-6.
[26]	滕飞, 张奇, 曲建升, 等. 基于专利竞争力指数和Doc-LDA主题模型的关键核心技术识别研究——以新能源汽车为例[J]. 数据分析与知识发现, 2024, 8(11), 33-46.
	TENG F, ZHANG Q, QU J S, et al.. Identification of key and core technologies based on patent competitiveness index and Doc-LDA topic model: a case study of new energy vehicles [J]. Data Anal. Knowledge Dis., 2024, 8(11), 33-46.
[27]	新牧网. 牧原: 饲料豆粕已降至6.9%, 未来要做无豆日粮[EB/OL]. (2022-09-22) [2024-04-26]. .
[28]	LIU C, PENG P, LI W, et al.. Deciphering variation of 239 elite japonica rice genomes for whole genome sequences-enabled breeding [J]. Genomics, 2021, 113(5): 3083-3091.
[29]	科技日报. 我母乳结构脂肪生产关键技术获突破[EB/OL]. (2023-12-20)[2024-04-26]. .
[30]	HUANG Z Y. Characterisation of dormancy cycling responses to environmental signals in contrasting Arabidopsis thaliana ecotypes [D]. Warwick: University of Warwick, 2013.