An artificial intelligence-based interference recognition method for an electrocardiogram, comprising: cutting and sampling heart beat data of a first data amount, and inputting the heart beat data to be recognized that is obtained by cutting and sampling into an interference recognition binary classification model for interference recognition; in a sequence of the heart beat data, performing signal anomaly determination on a heart beat data segment where an inter-beat interval is greater than or equal to a preset interval determination threshold value, so as to determine whether the heart beat data segment is an abnormal signal; if the heart beat data segment is not an abnormal signal, determining a starting data point and an ending data point of sliding sampling in the heart beat data segment according to a set time with a preset time width, and performing sliding sampling on the data segment from the starting data point until the ending data point so as to obtain multiple sampling data segments; and using each sampling data segment as heart beat data to be recognized for interference recognition.