Abstract: To realize the efficient recognition of critical information in unstructured process planning text, a named entity recognition model based on technology dictionary and neural network is established. Firstly, the technology dictionary and jieba word segmentation technology are comprehensively combined to realize automatic annotation of datasets, especially, the number and its identification letters are recognized as one unit in the automatic annotation of process parameter data, which enhances the effect of subsequent feature extraction. Secondly, the bidirectional long short term memory network is used to extract the feature of text information based on word2vec. Finally, conditional random field model is used to synthesize contextual logic to improve the recognition accuracy of critical process information. To verify the effectiveness of the proposed model, 431 work steps are utilized as training sample. Experimental results show that the values of accuracy rate, recall and F1 are 90.20%, 93.88% and 92.00% respectively, which has certain advantages compared with traditional models in the field. In addition, three experimental datasets from different technology books are tested, the results also show high robustness of the proposed model.