## 4 Results
### Similarity Calculation  
The topics obtained through the LDA model are further converted into word vector form using the Word2 vec model in this paper. In the specific operation, we adopt the skip-gram architecture and set relevant parameters, specifically, vector_size = 150, window = 5, min_count = 3, and epochs = 10. On this basis, we carry out the cosine similarity calculation for science and patent topics. A total of 5382 science-technology topics are finally obtained. Based on the above calculation results, we construct a similarity matrix. We adopt a threshold of 0.6686, representing the average similarity of all science-technology topics [4]. Whenever the similarity between a topic pair exceeds this threshold, it indicates collaboration between science and technology. According to statistics, there are a total of 3040 pairs of topics with similarities greater than 0.6686, accounting for about 56.48% of the total topic pairs. This further illustrates that there is a strong collaborative nature between science and technology in the field of cybersecurity. Drawing on the relevant strong and weak classification methods defined based on topic similarity in the related research [5], this paper divides the range of 0.6686 to 1 into three equal parts and classifies the relevance degree of topic pairs into three categories: weak correlation (from 0.6686 to 0.7791), moderate correlation (from 0.7792 to 0.8896), and strong correlation (from 0.8897 to 1). Among them, there are 1640 pairs of weakly correlated topics, 1337 pairs of moderately correlated topics, and 63 pairs of strongly correlated topics. To validate the similarity threshold of 0.6686, in this study, we employ the bootstrap resampling method to calculate its 95% confidence interval (CI). The results show that the CI [0.6648, 0.6726] fully contains the threshold, indicating that its statistical estimation as the mean collaboration between science and technology is robust and reliable. Additionally, we conduct an extended analysis of the quantile characteristics in the similarity distribution. The data reveal that the 65 th percentile (0.7518) and 95 th percentile (0.8648) naturally partition the distribution into three intervals, corresponding to 65%, 30%, and 5% of the sample size, respectively. This distribution characteristic aligns closely with the classification method employed in this study. Initially, the 95 th percentile (0.8648) approximates the lower bound of the strong correlation category (0.8897). Secondly, the similarity range of the moderate correlation category (from 0.7792 to 0.8896) exhibits a high overlap with the 30% quantile interval (from 0.7518 to 0.8648). This consistency validates the alignment between the classification method employed in this study and the inherent distribution patterns of the data. The similarity distribution and the intervals of weak, medium, and strong correlation categories are illustrated in Figure 5.  
We take 0.8897 as the threshold, select 63 pairs of strongly correlated topics with a similarity degree greater than or equal to 0.8897, and construct a high-similarity topic correlation evolution map in the form of a Sankey diagram, as shown in Figure 6. In the brackets beside each topic, S represents scientific topics and T represents technological topics. In the overall layout, scientific topics are placed in the middle position, while technological topics are arranged on the left and right sides. This arrangement can visually and clearly show the evolutionary process of strongly correlated topic pairs. To analyze the presented content more accurately and thoroughly, this paper takes scientific data (i.e., the middle column in Figure 6) as the core cl  
Figure 5: Similarity distribution and weak, moderate, and strong correlation category interval mapping. Figure 6: The evolution map of high-similarity topic with science–technology associations. the association and evolution relationship of science-technology topics in the field of cybersecurity.