基于K-近邻分类算法的供需数据智能匹配研究

Research on Intelligent Matching of Supply and Demand Data Based on K-Nearest Neighbor Classification Algorithm

针对将智能信息处理技术赋能于科技成果转移转化,研究应用大数据技术支撑科技成果转化体系中供需对接建设。利用各类高文档频率的特征选择方法,结合数据挖掘技术中的K-NN分类算法,分析并建立匹配模型,通过机器对转化数据进行智能匹配,与转化数据模型建立联系,为成果转化体系建立高效机制。通过利用文本分类系统的召回率指标对实验结果进行系统的分析,供需数据智能匹配率佳。通过对成果转化数据来源的真实性、准确性、完整性、时效性等研究分析得知,在保证数据来源质量基础上可发挥大数据处理和分析的作用,助力科技成果转化体系建设。

Aiming at the empowerment of intelligent information processing technology in the transfer and transformation of scientific and technological achievements,research and application of big data technology to support the construction of supply and demand in the transformation system of scientific and technological achievements.Using various feature selection methods with high document frequency,combined with the K-NN classification algorithm of the Data Mining Technology,analyze and establish a matching model.Intelligently match the conversion data through the machine,establish a connection with the conversion data model,and create an efficient mechanism for the establishment of the result conversion system.By using the recall rate index of the text classification system,the experimental results are systematically analyzed,and the intelligent matching rate of supply and demand data is good.Through the research and analysis of the authenticity,accuracy,completeness,and timeliness of the data source of the result conversion,it is known that the data source quality can play the role of big data processing and analysis to help the construction of the scientific and technological achievements conversion system.