With the continuous growth in demand for intelligent services, future 6G networks need to support higher communication efficiency and efficient intelligent connections. Semantic communication technology integrates the meaning of information into data processing and transmission, making it a potential paradigm for 6G. Considering that current research on semantic communication systems mainly focuses on the extraction and encoding of semantic features, with less attention to the impact of channel coding during the communication transmission process on system performance. Therefore, based on the CNN-Transformer (CT) semantic feature extraction and encoding scheme, this paper introduces a polar encoder, designing the end-to-end semantic CT-Polar communication system model framework. Through simulation verification, the CT-Polar designed in this paper demonstrated excellent performance in signal recovery on different datasets.