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The third generation sequencing data exposes great advantage on read length, which extremely benefits the genomic analyses. However, the third generation sequencing data implies error models different from the ones that the second generation data brings. It is suggested to correct sequencing errors, which could significantly reduce false positives in downstream analyses. Existing error correction approaches often suffer accuracy loss when the hybrid reads present diversity or the coverage varies. In this paper, we propose a novel method based on crowdsourcing strategy, which is implemented as CLTC. CLTC is also a hybrid correction algorithm, which consists of four steps. The second generation reads are first collected and mapped to the third generation reads. Then, the base difficult level is defined to describe the diversities on a base among a group of 2nd-generation reads covered it. The capability is …