对来源不同的地质对象进行关联匹配，并通过模型对其结构、属性及语义关系进行表示是后期语义查询及聚类等任务的重要支撑。文章针对地质调查空间实体与外部文本描述语义异构、表达差异等问题，提出了一种基于注意力机制的孪生网络地质调查空间实体与文本描述信息关联匹配模型。首先，将地质调查空间实体的属性信息转换成为文本段落，以句向量基本粒度对地质空间实体进行文本语义编码；接着将两类文本对象映射到统一向量空间中，并输入到孪生网络中进行特征学习，最后在构建真实数据集上进行模型性能的实验测评。结果显示，该模型能够较好表示地质调查空间实体句子语义信息，其识别F1值相比基准实验提高了8.4个百分点，优于选取的对比方法。

Association matching of geological objects with different sources and representation of their structures, attributes and semantic relationships by models is an important support for later tasks such as semantic query and clustering. In this paper, we propose a twin network geological survey spatial entities and text description information association matching model based on attention mechanism for the problems of semantic heterogeneity and expression differences between geological survey spatial entities and external text descriptions. First, the attribute information of geological survey spatial entities is converted into text paragraphs, and the text semantics of geological spatial entities is encoded with the basic granularity of sentence vectors; then the two types of text objects are mapped into a unified vector space and input to the twin network for feature learning, and finally the experimental evaluation of model performance is conducted on the constructed real dataset. The results demonstrate that the model can better represent the sentence semantic information of geological survey spatial entities, and its recognition F1 value is improved by 8.4 percentage points compared with the benchmark experiment, which is better than the selected comparison method.