基于OPAC搜索的高校图书馆图书资源需求预测

The Demand Prediction of Book Resources for University Library Based on OPAC Search

论文通过对高校图书馆OPAC搜索的数据挖掘,构建了以国图分类号划分的图书资源需求量预测模型。首先设计了针对OPAC关键词的分类方法,对读者外借、预约及OPAC搜索三个预测器的数据分布进行统计分析,然后完成基于搜索量的线性回归预测模型设计,并与基础预测模型进行伪样本外预测检验,验证了加入搜索关键词变量后的模型能够提供更准确的数据预测结果。最后,提出了基于需求预测结果的采购决策模型,为图书采购的比例分配提供数据参考。

Based on the data mining of OPAC search in university library,this paper constructs a book resource demand forecasting model divided by national library classification number.First,we design a classification method for OPAC keywords,and make statistical analysis on the data distribution of three predictors:reader lending,reservation and OPAC search.Then,we complete the design of linear regression prediction model based on search volume,and carry out the false sample prediction test with the basic prediction model,which verifies that the model after adding the variables of search keywords can provide more accurate data prediction results.Finally,the paper proposes a purchase decision-making model based on the result of demand forecast,which provides data reference for the proportion distribution of book purchasing.