The main task of text segmentation is to divide the text into several relatively independent text blocks according to the topic relevance. Aiming at the shortcomings of the existing text segmentation models in extracting fine-grained features such as text paragraph structural information， semantic correlation and context interaction， a text segmentation model TS-GCN （Text Segmentation-Graph Convolutional Network） based on Graph Convolutional Network （GCN） was proposed. Firstly， a text graph based on the structural information and semantic logic of text paragraphs was constructed. Then， the semantic similarity attention was introduced to capture the fine-grained correlation between text paragraph nodes， and the information transmission between high-order neighborhoods of text paragraph nodes was realized with the help of GCN， so that the model ability of multi-granularity extraction of text paragraph topic feature representations was enhanced. The proposed model was compared with the representative model CATS （Coherence-Aware Text Segmentation）， and its basic model TLT-TS （Two-Level Transformer model for Text Segmentation）， which were commonly used as benchmarks for text segmentation task. Experimental results show that TS-GCN’s evaluation index P_k is 0.08 percentage points lower than that of TLT-TS without any auxiliary module on Wikicities dataset. And the proposed model has the P_k value decreased by 0.38 percentage points and 2.30 percentage points respectively on Wikielements dataset compared with CATS and TLT-TS. It can be seen that TS-GCN achieves good segmentation effect.