Abstract: Pacific BioScience (PacBio) long-read sequencing data exhibit relatively high error rates, typically ranging from 10% to 15%. Existing self-correction methods generally require high sequencing depth and may discard or fragment reads during the correction process. To address these limitations, a deep learning-based error correction method, termed PBECToo, is proposed. PBECTool consists of three stages: sequence encoding, model training, and correction result generation. A novel encoding method is designed to transform sequence alignment information, reference genome data and sequencing quality scores into a stacked image representation, thereby reformulating error correction as an image classification problem. Finally, corrected FASTA sequences are generated according to the model inference results. PBECTool achieved accuracies exceeding 99% on the E. coli dataset and 97% on the S. cerevisiae dataset, outperforming all competing methods. It also achieved superior performance in terms of output depth, genome coverage, and corrected read length. Experimental results demonstrate the effectiveness of PBECTool for correcting low-depth third-generation sequencing data.