Abstract: The construction information of concrete dams is mostly expressed in form of document text, which is characterized by a wealth of information, wide distribution, and complex internal relations; manual operation finds it difficult to accurately extract information knowledge and sort out complicated relationships of construction information. In natural language processing, named entities are the carriers of text information, and realizing accurate and fast entity recognition is an important premise of construction knowledge mining. This paper describes a knowledge intelligent recognition and analysis method that combines deep learning and association rule technique for processing the construction documents of concrete dams. The types of concrete dam construction entities are defined; the bi-directional long-short term memory (Bi-LSTM) and conditional random field (CRF) methods are used to build named entity recognition models and generate construction entity knowledge sets. Further, we develop an entity association rule extraction technique by considering the expression rules and entity types of the text, predefining the relationships between the entities, and determining their combination forms. And we use this method to improve the Apriori algorithm and obtain strong association rules by calculating the frequent itemset. Application to the weekly report text for construction supervision of a concrete dam verifies the method, and shows its accuracy of 86.4% in recognition of named entities. The improved Apriori algorithm is used to analyze the association rules between the entities, demonstrating its advantages and usefulness in raising the intelligence and refinement level of document knowledge extraction and analysis for concrete dam construction.

Key words: concrete dam, construction document, named entity, intelligent recognition, deep learning, knowledge mining