Abstract: With the advent of intelligent manufacturing and big data, the Made in China 2025 Initiative and Industry 4.0 have been paying increasing attention to automation and intelligent industrial equipment. In the background of the present times, the complexity and intelligence of computer numerical control (CNC) machine tools have been continuously improved, and the types and descriptions of CNC machine tools’ faults have increased, presenting serious challenges to equipment maintenance and diagnosis of CNC machine tools. In order to provide guarantee for accurate fault diagnosis of CNC machine tools, and to prolong the service life of CNC machine tools, it is necessary to improve the performance of named entity recognition system. Accordingly, the named entity recognition in the equipment and faults field of CNC machine tools were studied, taking the historical examinations and repair records of CNC machine tools as the research object. After analyzing the characteristics of fault description in the historical examinations and repair records, a named entity recognition method was proposed based on the combination of bidirectional long short-term memory (BLSTM) and conditional random field with loop (L-CRF). The first step is to input a sentence and segment and label the input sentence. The annotation corpus is combined with the pre-trained generated word vector by using Skip-gram model in Word2vec, and the word vector is converted into a word vector sequence through the word embedding layer. In the second step, the word vector sequence is integrated into the BLSTM layer to learn long term dependency information. The final step is to input the sentence expression into the L-CRF layer to obtain the global optimal sequence. The experimental results show that the method is superior to other named entity recognition methods, which lays a solid foundation for the intelligent maintenance and the real-time diagnostic tasks of CNC machine tools.