## 4 Empirical Analysis of the Uncertainty Index System  
### Theme Exploration  
In LDA topic training, the number of topics K must be preset in advance and the optimal number of topics is determined by using the perplexity (perplexity) topic number curve or calculating the similarity between topics [51]. In this paper, we use the perplexity evaluation method to determine the optimal number of topics. Its calculation formula is shown in Equation (1).  
\[\text{perplexity }(\text{D})=\exp(-\frac{\sum_{i=1}^{M}\log_{p}(d_{i})}{ \sum_{i=1}^{M}N_{i}}) \tag{1}\]  
where D is the test set, \(M\) is the number of texts, \(d_{i}\) is the sequence of words in document d and \(N_{i}\) is the number of words in document \(d\). The degree of confusion indicates the uncertainty of the subordinate topics of document d and reflects the degree of model fit, so, theoretically, the smaller the degree of confusion, the better the model fit. However, in practice, the elbow-folding method is generally used to determine that the lowest point of the degree of confusion, or the inflection point corresponding to K, is the optimal number of topics.  
Figure 1: Research Step.  
As can be seen from Figure 2, with the increase in the number of topics, the pre-confusion degree decreases significantly, indicating that the model fits better and can effectively achieve topic mining. Assuming that the topic number interval is [0,17.5] [45], the step size is 2.5 and the topic number is taken as the abscissa and the confusion value as the ordinate and a two-digit result graph is drawn. It is determined that the optimal topic number of the LDA model in this paper is K = 13. LDA is an unsupervised machine learning method, so its mining effect of mining potential topics in documents is related to the number of iterations; the higher the number of model iterations, the better the convergence effect of the model. Combined with practical considerations, the number of convergence was set at 500 in this paper [52].  
According to the calculation of confusion degree, 13 themes of digital technology application in construction industry and the high probability distribution of keywords of each theme are obtained by using the Python programming language of LDA model, as shown in Table 2. The five high-frequency keywords under each topic were sorted out and we summarise the theme identification of the high-frequency words that most accord with the current topic. Based on the combination of the LDA model identification results and the literature review results, 13 topics were finally selected.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Theme** & **Subject Heading** \\ \hline Topic 1 & Government, Policy, Administration, Reform, Safeguards \\ Topic 2 & Industry, System, Administration, Path, Execution \\ Topic 3 & Transformation, Reform, Upgrade, Regulatory, Management \\ Topic 4 & Standards, Levels, Indicators, Operational, Systems \\ Topic 5 & Organisation, Operations, Markets, Strategy, Capabilities \\ Topic 6 & Industry, Approach, Stage, Structure, Main \\ Topic 7 & Science and Technology, Capacity, Talent, Intelligence, Building \\ Topic 8 & Understanding, Impact, Innovation, Driving, Pathways \\ Topic 9 & Outcome, Data, platform, Project, Consultancy \\ Topic 10 & Operations, Strategy, Costs, Dynamics, Finance \\ Topic 11 & Technology, Achievements, Upgrade, Construction, Costs \\ Topic 12 & Outcome, Property Rights, Legal, Innovation, Core Technology \\ Topic 13 & Management, Digital, Contracts, Standardisation, Power and Responsibility \\ \hline \hline \end{tabular}
\end{table}
Table 2: Topic distribution of high-probability feature words.  
Figure 2: Perplexity calculation result.