A log parsing method, comprising: - acquiring, by a processor, sample log data; - performing, by a processor, according to a length and beginning and ending keywords of each sample log in the sample log data, a clustering processing on the same log in the sample log data to obtain a plurality of log clusters; - determining, by a processor, a quality score of each log cluster of the plurality of log clusters; and - parsing, by a processor, a target log to be parsed by using the plurality of log clusters and the quality score; - wherein, the quality score is configured to determine an adaptive similarity threshold for the target log when parsing the target log; - wherein determining, by the processor, the quality score of the each log cluster of the plurality of log clusters comprises: - determining the quality score of the each log cluster according to a compactness of each log cluster of all log clusters and a separation between different log clusters; - wherein the compactness is defined as a ratio of a number of constant fields to a total length in the target log, and the separation is defined as a discrepancy between discrete item sets of two different target logs; - wherein a discrete item set in the discrete item sets refers to a set of discrete item pairs, and the discrete item pairs refer to all item pairs generated by every two fields in each target log, the discrepancy refers to Jaccard distances of discrete item pairs corresponding to different target logs; - wherein the quality score is a normalized product of the compactness and the separation. - acquiring, by a processor, sample log data; - performing, by a processor, according to a length and beginning and ending keywords of each sample log in the sample log data, a clustering processing on the same log in the sample log data to obtain a plurality of log clusters; - determining, by a processor, a quality score of each log cluster of the plurality of log clusters; and - parsing, by a processor, a target log to be parsed by using the plurality of log clusters and the quality score; - wherein, the quality score is configured to determine an adaptive similarity threshold for the target log when parsing the target log; - wherein determining, by the processor, the quality score of the each log cluster of the plurality of log clusters comprises: - determining the quality score of the each log cluster according to a compactness of each log cluster of all log clusters and a separation between different log clusters; - wherein the compactness is defined as a ratio of a number of constant fields to a total length in the target log, and the separation is defined as a discrepancy between discrete item sets of two different target logs; - wherein a discrete item set in the discrete item sets refers to a set of discrete item pairs, and the discrete item pairs refer to all item pairs generated by every two fields in each target log, the discrepancy refers to Jaccard distances of discrete item pairs corresponding to different target logs; - wherein the quality score is a normalized product of the compactness and the separation. - wherein, the quality score is configured to determine an adaptive similarity threshold for the target log when parsing the target log; - wherein determining, by the processor, the quality score of the each log cluster of the plurality of log clusters comprises: - determining the quality score of the each log cluster according to a compactness of each log cluster of all log clusters and a separation between different log clusters; - wherein the compactness is defined as a ratio of a number of constant fields to a total length in the target log, and the separation is defined as a discrepancy between discrete item sets of two different target logs; - wherein a discrete item set in the discrete item sets refers to a set of discrete item pairs, and the discrete item pairs refer to all item pairs generated by every two fields in each target log, the discrepancy refers to Jaccard distances of discrete item pairs corresponding to different target logs; - wherein the quality score is a normalized product of the compactness and the separation. - determining the quality score of the each log cluster according to a compactness of each log cluster of all log clusters and a separation between different log clusters; - wherein the compactness is defined as a ratio of a number of constant fields to a total length in the target log, and the separation is defined as a discrepancy between discrete item sets of two different target logs; - wherein a discrete item set in the discrete item sets refers to a set of discrete item pairs, and the discrete item pairs refer to all item pairs generated by every two fields in each target log, the discrepancy refers to Jaccard distances of discrete item pairs corresponding to different target logs; - wherein the quality score is a normalized product of the compactness and the separation.