## 5 Discussion
Take the list of candidate words as a test set and recommend them separately to get the candidate list of the top 30 items of similarity of each keyword. Use the model as a source to construct the true set of recommendation data. For one of the classes block1, the classes associated with it and its own attributes are used as its recommendation items. More specifically, using class block1 as a node, select the two levels of abstraction upward and the two levels of detail downward, and include the \"parent\", \"child\" directly associated with block1, and the indirectly associated \"sibling\" classes, as well as \"parent\" of \"parent\" classes, and \"child\" of \"child\" classes as the true set of recommended items. As an example, the recommendations for spacecraft include all attributes, the subsystem classes and all classes contained in the subsystem classes. Based on this, 100 inputs are randomly tested to judge the number of times out of 100 recommendations are valid. The validity is judged by evaluating a recommendation as valid if there is a truth value in the result based on one of the inputs. This section uses the GLOVE pre-trained model and evaluates the effectiveness of recommendations by comparing the pre-feedback and post-feedback results, respectively, using the user's initial feedback as a cutoff. A comparison was attempted using different pre-training models Word2 Vec and bert. The results of the three pre-trained models before and after feedback are evaluated as shown in Table 4 below. Recommendations were made using Minkowski similarity as well as cosine similarity respectively, and it can be seen that the effectiveness of recommendations using cosine similarity is higher. And after one feedback, the effectiveness of recommendation is improved accordingly. This method integrates two sources of information, domain knowledge and model data, and better utilizes SysML models to make recommendations more authentic and \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Similarity Metric**} & **Recommendation Effectiveness** & **Recommendation Effectiveness** \\  & **(before Feedback)** & **(after Feedback)** \\ \hline Cosine similarity & 69.1\% & 70.2\% \\ Minkowski similarity & 65.5\% & 67.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of the results. Figure 4: Testing with spacecraft as an example. (**a**) Creating the spacecraft block. (**b**) On this basis, the recommended items were selected according to Table 2 and added to the model. Figure 5: SysML model constructed based on recommendations. reliable. The manually constructed models are used in the study as the historical model set for example only. During the actual application of the method, the SysML models constructed based on the guidance of domain experts can be used as the training data source and combined with the specialized domain knowledge as the input to make authentic and reliable recommendations. Subsequently, the SysML models constructed based on the recommendations can be used as historical model data with new and increasing domain knowledge or specifications to continuously fine-tune the GLOVE model, gradually enrich the training dataset, and utilize the database to record the model evolution process in the full lifecycle.