## 5 Discussion  
Regarding the specific implementation of the recommendation process, this study utilized the GLOVE implementation written in Python by Stanford. The generality of the approach is more clearly demonstrated by the fact that the following three domain texts were randomly selected to be obtained as training data by searching for spacecraft-related domains. The domain knowledge documents used here are \"Spacecraft dynamics and control: an introduction\" [52], \"Spacecraft thermal control\" [53] and \"Standard materials and processes requirements for spacecraft\" [54]. These domain knowledge documents are readily accessible on Google and the data are publicly available. The parameter settings for the GLOVE model are shown in Table 2 below. no_components represents the number of latent dimensions used to express the underlying features of the data, and learning_rate is the learning rate for stochastic gradient descent (SGD) estimation, controlling the update speed of model parameters during training. The remaining parameters are set to the default values of the GLOVE model. Because the task complexity in this study is moderate, 100-dimensional word vectors are sufficient to capture the key semantic relationships between words without the need for higher dimensions. Moreover, the amount of training data is small and using higher word vector dimensions may lead to overfitting. After inputting a word or phrase from the console, a corresponding recommendation list is obtained, and the candidate items are sorted and displayed according to the similarity of the word vectors, and the similarity of the corresponding candidate words is output. From this, we can judge whether it belongs to an entity or an attribute based on the recommended concepts, and select entities and attributes in turn according to the criteria of constructive models so as to update the model content and construct a new SysML model to assist modelers in modeling. We set the number of recommended items to 30, with the first 10 being the most similar phrases. Based on the fine-tuned GLOVE model word embeddings, the input \"spacecraft\" generates the following recommended output list, as shown in Table 3.  
From this, we can select entities and attributes, and based on the selection, obtain the recommended relationships between entities. As shown in Figure 4a,b, spacecraft was taken as an example and selected based on the recommended list. Based on this approach, a new SysML model is constructed sequentially based on the contents of the recommendation list. For example, Figure 5 shows the model constructed based on recommendations, combined with personnel selection. The selection of this model element is based on personal judgment and actually simulates the process of selecting based on recommendations, thus constructing a new SysML model. Subsequently, the SysML model constructed based on recommendations can be used as historical model data to enrich the training dataset, making recommendations more accurate. \begin{table}
\begin{tabular}{c c} \hline
**Initialization Parameter** & **Value** \\ \hline no\_components & 100 \\ learning\_rate & 0.05 \\ Window\_size & 10 \\ \hline \end{tabular}
\end{table} 
Table 2: GLOVE model parameterization. \begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Candidate Term** & **Similarity** \\ \hline
1 & propulsion subsystem & 0.700057 \\
2 & power subsystem & 0.649774 \\
3 & power mgmt sw & 0.388500 \\
4 & tank & 0.385118 \\
5 & avionics subsystem & 0.377041 \\
6 & mission context & 0.318995 \\
7 & launch vehicles & 0.287502 \\
8 & software subsystem & 0.277133 \\
9 & baze layer & 0.269542 \\
10 & flight crew & 0.231632 \\
11 & stellar & 0.229464 \\
12 & space physics & 0.229332 \\
13 & heat exchange & 0.220312 \\
14 & combustion gases & 0.219325 \\
15 & gn\&c subsystem & 0.215557 \\ \hline \end{tabular}
\end{table} 
Table 3: 15 Recommendations based on “spacecraft”.