The invention discloses an electrocardio normality/abnormality big-data processing method and device, and the method employs a normal electrocardio database, and comprises the following steps: S1, partitioning to-be-classified electrocardio data according to cardiac beat, and then carrying out the normalization processing of length and amplitude, thereby forming a plurality of pieces of beat-wave-shaped data; S2, extracting the index data of to-be-classified electrocardio data; S3, determining a confidence interval according to the index data stored in the database, comparing the index data of the extracted to-be-classified electrocardio data with the confidence interval, and outputting a comparison result; S4, calculating the similarity of the plurality of pieces of beat-wave-shaped data obtained through the partitioning of the to-be-classified electrocardio data with waveform data, corresponding to cardiac beat, in the electrocardio data stored in the database, comparing the similarity with a similarity threshold value, and outputting the comparison results. The device comprises the normal electrocardio database, and a plurality of modules which are used for achieving the above steps. The device can achieve the reliable classification and screening of the electrocardiogram which is not diagnosed, and avoids false negative.