In order to solve the problems of objectivity and inaccuracy in the current evaluation of marine engine simulator, an evaluation model is established based on C-OWA (combination ordered weighted averaging)-the Coefficient of Variation method-minimum discriminant information principle combination weighting method and Vague set. Taking standby of marine generator project as an experiment example, the evaluation index system is established, and the evaluation model is applied to evaluate each evaluation team, and the final operation score of each team is obtained. The results show that (1) the index system is comprehensive and targeted for the operation evaluation of generator standby project; (2) the combination weighting method can improve the rationality of index weighting; (3) Vague set fuzzy decision-making can improve the accuracy of fuzzy evaluation of simulator operation; (4) the model shows rationality and reliability in evaluation results and can provide a reasonable and feasible method for intelligent evaluation of marine simulator.

Aiming at the problem of fuzziness and randomness in the evaluation process of marine engine simulator, a comprehensive evaluation model based on subjective and objective combination weighting method and cloud model method is established on the basis of single subjective and objective combination weighting method. Firstly, the combined weighting method is used to determine the comprehensive weight; Then, the standard cloud model is generated by using the cloud model method, and the digital characteristics of the evaluation cloud are obtained through the sample data and cloud computing rules. The digital characteristics of the evaluation cloud are obtained by the backward cloud generator and comprehensive weight; Finally, the similarity between the generated evaluation cloud and the standard cloud model is calculated, so as to evaluate the operation of the student marine engine simulator. The example analysis shows that the evaluation method can scientifically and reasonably evaluate the crew’s operation ability, and is feasible and effective.

With the development of intelligentization in maritime vessels, the pursuit of an organized and scalable knowledge storage approach for marine engine room systems has become one of the current research hotspots. This study addressed the foundational named entity recognition (NER) task in constructing a knowledge graph for marine engine rooms. It proposed an entity recognition algorithm for Chinese semantics in marine engine rooms that integrates language models. Firstly, the bidirectional encoder representation from transformers (BERT) language model is used to extract text features and obtain word-level granularity vector matrices. Secondly, the trained word embeddings are fed into a bidirectional long short-term memory network (BiLSTM) to extract contextual information. It considers the surrounding words and their sequential relationships, enabling a better understanding of the context. Additionally, the conditional random field (CRF) model was used to extract the globally optimal sequence of named entities in the marine engine room semantic. The CRF model considered the dependencies between adjacent entities that ensured a coherent and consistent final result for entity recognition in marine engine room semantics. The experiment results demonstrate that the proposed algorithm achieves superior F1 scores for all three entity types. Compared with BERT, the overall precision, recall, and F1 score of the entity recognition are improved by 1.36%, 1.41%, and 1.38%, respectively. Future research will be carried out on named entity recognition of a small sample set to provide basic support for more efficient entity relationship extraction and construction of a marine engine room knowledge graph.