## 3 Results
### Factors Influencing AI Education Sentiment Attitudes via MGWR  
This study employs a multiscale geographically weighted regression (MGWR) with an adaptive bandwidth optimized using AICc, iterated 200 times until convergence (termination criterion: 1.0 \(\times\) 10\({}^{-5}\)). Based on 13 standardized variables, the model analyzes the positive and negative emotional attitudes of the public across 31 observation points, exploring spatial heterogeneity and differences in driving mechanisms to reveal the spatial distribution characteristics of emotions.  
The global regression results indicate that the MGWR model explains 96.7% of the variance, demonstrating a high goodness of fit (R\({}^{2}\) = 0.967, adjusted R\({}^{2}\) = 0.946). This suggests that the model effectively captures the relationships between variables. Additionally, the degree of dependence (DoD) is close to 1, indicating that the fitted model effectively explains the spatial dependence of emotional attitudes, validating its advantage in capturing spatial non-stationarity.  
Bandwidth analysis results show that in the positive emotion model, except for F6 (bandwidth: 712 km, ENP = 4.059, \(\beta\) range [\(-\)0.102, 0.482], standard deviation 0.129), other variables such as F2, F8, and F7 have bandwidths exceeding 9976 km, with ENP values close to 1 and minimal parameter variation (e.g., F8 \(\beta\) = 0.673), exhibiting a global effect. In the negative emotion model, the bandwidth of F6 slightly increases to 729 km (ENP = 3.917, \(\beta\) range [\(-\)0.138, 0.341], standard deviation 0.141), still showing significant local variation. Meanwhile, F5 (bandwidth: 4417 km, \(\beta\) range [0.055, 0.061]) also reveals some spatial differences, whereas other variables maintain bandwidths exceeding 9975 km (e.g., F8 \(\beta\) = 0.702), exhibiting nearly uniform effects.  
In comparison, F8 serves as the strongest positive factor in both emotion models (positive: \(\beta\) = 0.797; negative: \(\beta\) = 0.854), while F7 consistently acts as a strong negative factor (positive: \(\beta\) = \(-\)0.471; negative: \(\beta\) = \(-\)0.617). However, the effects of F4 and F6 exhibit significant differences: F4 is not significant for positive emotions but has a strong positive effect on negative emotions (\(\beta\) = 0.508, \(p\) = 0.002). In contrast, F2 and F6 are significant for positive emotions (\(\beta\) = 0.366, \(p\) < 0.001; \(\beta\) = 0.252, \(p\) = 0.017) but show weakened effects on negative emotions (\(\beta\) = 0.175, \(p\) = 0.062; \(\beta\) = 0.147, \(p\) = 0.170), highlighting their prominent local variability in both emotion models. Other variables, such as F3, F5, and F9-F13, do not exhibit significant effects in either model.  
Furthermore, after refining the model at a local scale, the total residual sum of squares decreases to 0.577, and the coefficient of determination (R\({}^{2}\)) improves to 0.983. This further confirms that accounting for spatial locality significantly enhances the explanatory power of the model. Most variables, such as F2, F7, and F8, have bandwidths close to approximately 9976 km, suggesting that their influence remains relatively consistent over a large spatial extent. However, the bandwidths of F5 and F6 decrease to approximately 4417 km and 729 km, respectively, with a relatively high number of effective parameters (ENP = 4.059), indicating significant spatial variation in their effects across different regions.