Factors and Models Influencing Value Co-Creation in the Supply Chain of Collection Resources for Library Distribution Providers Under Data Ecology

Under the data ecology, the advancement of relevant technology and the utilization of relevant resources have provided more efficient technical services for various industries. However, with the proliferation of data resources, problems such as information pollution and data redundancy have arisen in the process of supply chain services for collection resources. For solving such problems and enhancing the collection resource supply efficiency of librarians, the study uses data mining technology combined with improved K-Means clustering algorithm to design a value co-creation model of library collection resource supply chain for librarians under data ecology. The outcomes indicate that the shortest running time of traditional K-Means algorithm is 40ms and the longest running time is 115ms in Wine dataset, and the running time of improved K-Means algorithm is stable at 59ms; the shortest running time of traditional K-Means algorithm is 26 ms and the longest running time is 58ms in Iris dataset, and the running time of improved K-Means algorithm is stable at 53 ms. The clustering accuracy of the improved K-Means algorithm in the Wine data set is 98.2%, which is 0.3% exceeding the traditional K-Means algorithm, which is 97.9%; the clustering accuracy in the Iris data set is 100%, which is 2.4% exceeding the traditional K-Means algorithm, which is 97.6%. In summary, it can be seen that the studied data ecology has a good application of the factors and models influencing the value co-creation of the supply chain of library resources for library dispensers.