Prototype Algorithm: Number Chain Features in Spatial Similarity Calculation of Time-Series Graph Sources

In this paper, we propose a method to calculate spatial semantic similarity based on sampled same-area time-series images. First, we preprocess the corpus data containing spatial information, then we project the coordinates in the preprocessed corpus data to obtain the actual spatial ranges, then we determine the contextual annotations of geographical feature gestures in the time-series images and perform comparison sampling, and finally we calculate the similarity between the coordinates of the overall same-area time-series image geo-corpus The similarity is evaluated for each two nodes in the set of the overall same-region geo-serial corpus. This paper identifies the causes of data noise and interpretation bias arising from the migration process of geographic data in time-series images, which effectively complements the traditional natural semantic similarity model and improves the effectiveness of intelligent geographic information retrieval and ancient landscape painting verification.