## 3 Results  
### Spatial Distribution Characteristics of AI Education Sentiment Attitudes  
In the context of sentiment analysis, the results of this study align with previous research, showing that users with positive emotions generally outnumber those with negative emotions [32; 33]. The gap between positive and negative sentiments remains roughly three times larger in all 31 administrative regions. This suggests that users on major Chinese social media platforms generally exhibit positive reactions to the application of AI in education. In terms of regional performance, the calculation of the imbalance index (S) (Table 2) shows that China's positive and negative sentiment attitudes towards AI education are 0.26 and 0.29, respectively. This indicates significant regional disparities in both positive and negative sentiments towards AI in education, with the regional variation in negative sentiment being slightly larger than that of positive sentiment. To further explore this disparity, Moran's I and Getis-Ord Gi* indices were introduced to measure whether the two sentiment attitudes exhibit global and local spatial autocorrelation, respectively. Moran's I is used to assess the overall pattern and trend of the data. If the values in the dataset tend to cluster spatially (i.e., high values cluster near other high values, and low values cluster near other low values), the Moran's I index will be positive. Getis-Ord Gi* is used to assess each element within the context of neighboring features. When global spatial autocorrelation is not significant, local spatial autocorrelation can be identified by pinpointing locations where anomalies or strong influence points may be hidden. The Moran's I indices for China's positive and negative sentiment attitudes towards AI education are 0.15 and 0.17, respectively, both greater than 0, with Z-Score results passing the significance test. This indicates that, from a spatial proximity perspective, both positive and negative sentiment attitudes exhibit imbalance and spatial aggregation, further confirming the spatial clustering of sentiment attitudes towards AI education in China. The results from Getis-Ord Gi* detection show that this spatial clustering is characterized by high-value aggregation, meaning that regions with high positive sentiment scores are likely to have neighboring regions with similarly high scores. Additionally, in contrast to the overall sentiment trend, negative sentiment demonstrates more significant spatial autocorrelation than positive sentiment. This suggests that negative sentiment is more prone to diffusion and spread between groups and regions compared to positive sentiment. The research results above reveal that sentiment attitudes towards AI education in China exhibit significant spatial clustering and regional differences. To present the distribution characteristics of sentiment attitudes across different administrative regions more intuitively, this study further employs visual methods to display the positive and negative sentiment attitudes of AI education across the 31 provinces of China through visual maps, as shown in Figure 1 (Positive Sentiment) and Figure 2 (Negative Sentiment). In the visual maps, warmer colors (such as red and orange) represent higher emotional intensity, while cooler colors (such as blue) indicate lower emotional intensity. From the figures, we can observe significant spatial differences in sentiment attitudes towards AI education across the 31 administrative regions. Specifically, the eastern coastal regions (such as Beijing, Guangdong, Shanghai, and Zhejiang) show higher positive sentiment scores, indicating that users in these areas generally hold optimistic and supportive attitudes towards AI education functions and applications. In the central regions, such as Shanxi, Sichuan, Chongqing, and Hubei, the sentiment attitudes are also relatively positive.