摘要：【中文】本发明公开了一种基于卷积神经网络的图书采购推荐方法及装置，首先，获取历史书单数据和历史订单数据；然后将历史书单数据与历史订单数据进行匹配，根据匹配结果将历史书单数据中的图书划分为购买和未购买两个类别；接着从带有标签的历史书单数据划分出训练集；接下来将训练集中包含的信息转化为向量的表示形式；再利用训练集对应的向量对预先构建的神经网络模型进行训练；最后将待处理图书的信息转换为向量后，传入训练好的神经网络模型，得到待处理图书的采购推荐结果。本发明的方法可以利用构建的神经网络模型对图书进行购买推荐，并实现了推荐效率以及推荐准确性的提高。 【EN】The invention discloses a book purchasing recommendation method and device based on a convolutional neural network, and the method comprises the following steps of firstly, acquiring history book order data and historical order data; then matching the historical book order data with the historical order data, and dividing the books in the historical book order data into two categories of purchase and unpurchased according to the matching result; then, marking out a training set from the data of the history list with the label; then converting the information contained in the training set into a vector representation form; then, training a pre-constructed neural network model by using vectors corresponding to the training set; and finally, converting the information of the book to be processed into vectors, and transmitting the vectors into the trained neural network model to obtain a purchasing recommendation result of the book to be processed. The method can utilize the built neural network model to purchase and recommend the book, and improves the recommendation efficiency and the recommendation accuracy.