The　emergence　of　the　third　generation　sequencing　technology,　featuring　longer　read　lengths,　has　demonstrated　great　advancement　compared　to　the　next　generation　sequencing　technology　and　greatly　promoted　the　biological　research.　However,　the　third　generation　sequencing　data　has　a　high　level　of　the　sequencing　error　rates,　which　inevitably　affects　the　downstream　analysis.　Although　the　issue　of　sequencing　error　has　been　improving　these　years,　large　amounts　of　data　were　produced　at　high　sequencing　errors,　and　huge　waste　will　be　caused　if　they　are　discarded.　Thus,　the　error　correction　for　the　third　generation　sequencing　data　is　especially　important.　The　existing　error　correction　methods　have　poor　performances　at　heterozygous　sites,　which　are　ubiquitous　in　diploid　and　polyploidy　organisms.　Therefore,　it　is　a　lack　of　error　correction　algorithms　for　the　heterozygous　loci,　especially　at　low　coverages.In　this　article,　we　propose　a　error　correction　method,　named　QIHC.　QIHC　is　a　hybrid　correction　method,　which　needs　both　the　next　generation　and　third　generation　sequencing　data.　QIHC　greatly　enhances　the　sensitivity　of　identifying　the　heterozygous　sites　from　sequencing　errors,　which　leads　to　a　high　accuracy　on　error　correction.　To　achieve　this,　QIHC　established　a　set　of　probabilistic　models　based　on　Bayesian　classifier,　to　estimate　the　heterozygosity　of　a　site　and　makes　a　judgment　by　calculating　the　posterior　probabilities.　The　proposed　method　is　consisted　of　three　modules,　which　respectively　generates　a　pseudo　reference　sequence,　obtains　the　read　alignments,　estimates　the　heterozygosity　the　sites　and　corrects　the　read　harboring　them.　The　last　module　is　the　core　module　of　QIHC,　which　is　designed　to　fit　for　the　calculations　of　multiple　cases　at　a　heterozygous　site.　The　other　two　modules　enable　the　reads　mapping　to　the　pseudo　reference　sequence　which　somehow　overcomes　the　inefficiency　of　multiple　mappings　that　adopt　by　the　existing　error　correction　methods.To　verify　the　performance　of　our　method,　we　selected　Canu　and　Jabba　to　compare　with　QIHC　in　several　aspects.　As　a　hybrid　correction　method,　we　first　conducted　a　groups　of　experiments　under　different　coverages　of　the　next-generation　sequencing　data.　QIHC　is　far　ahead　of　Jabba　on　accuracy.　Meanwhile,　we　varied　the　coverages　of　the　third　generation　sequencing　data　and　compared　performances　again　among　Canu,　Jabba　and　QIHC.　QIHC　outperforms　the　other　two　methods　on　accuracy　of　both　correcting　the　sequencing　errors　and　identifying　the　heterozygous　sites,　especially　at　low　coverage.　We　carried　out　a　comparison　analysis　between　Canu　and　QIHC　on　the　different　error　rates　of　the　third　generation　sequencing　data.　QIHC　still　performs　better.　Therefore,　QIHC　is　superior　to　the　existing　error　correction　methods　when　heterozygous　sites　exist.