A method for training a convolutional neural network based on sample distribution improvement, comprising following steps: a collection step of collecting enough lead-II heartbeat signals from a marked 12-lead electrocardiogram signal to form an initial dataset, wherein the lead-II heartbeat signals comprise a premature ventricular heartbeat signal and a non-premature ventricular heartbeat signal that each account for half of a total quantity of lead-II heartbeat signals; and setting a tag of the premature ventricular heartbeat signal to a, and a tag of the non-premature ventricular heartbeat signal to b; a statistics step of taking statistics on all heartbeat data in the collected dataset to obtain an average value, a maximum value, a minimum value, a peak, and a kurtosis value of each piece of heartbeat data to form an average value array AVG={a1, a2, a3,..., aN}, a maximum value array MAX={m1, m2, m3,..., mN}, a minimum value array MIN={c1, c2,..., cN}, a peak array F={f1, f2,..., fN}, and a kurtosis value array Q={q1, q2,..., qN}, and then calculating average values and standard deviations of the average value array, the maximum value array, the minimum value array, the peak array, and the kurtosis value array, which are denoted as x avg, σ avg, x MAX, σ MAX, x MIN, σ MIN, x F, σ F, x Q, and σ Q respectively; an elimination step of determining whether following conditions are met for all heartbeat data in each piece of data: x avg - 3 σ avg < x avg < x avg + 3 σ avg, x MAX - 3 σ MAX < x MAX < x MAX + 3 σ MAX, x MIN - 3 σ MIN < x MIN < x MIN + 3 σ MIN, x F - 3 σ F < x F < x F + 3 σ F, and x Q - 3 σ Q < x Q < x Q + 3 σ Q, obtaining a number of the conditions satisfied, and eliminating heartbeat data satisfying less than three of the above conditions in initial data to form a new heartbeat dataset; a repetition step of repeating the statistics step until a heartbeat dataset does not change, and using the heartbeat dataset as training data; and an input step of inputting the training data and a corresponding tag to a convolutional neural network for training, to obtain a convolutional neural network model based on sample distribution improvement. a collection step of collecting enough lead-II heartbeat signals from a marked 12-lead electrocardiogram signal to form an initial dataset, wherein the lead-II heartbeat signals comprise a premature ventricular heartbeat signal and a non-premature ventricular heartbeat signal that each account for half of a total quantity of lead-II heartbeat signals; and setting a tag of the premature ventricular heartbeat signal to a, and a tag of the non-premature ventricular heartbeat signal to b; a statistics step of taking statistics on all heartbeat data in the collected dataset to obtain an average value, a maximum value, a minimum value, a peak, and a kurtosis value of each piece of heartbeat data to form an average value array AVG={a1, a2, a3,..., aN}, a maximum value array MAX={m1, m2, m3,..., mN}, a minimum value array MIN={c1, c2,..., cN}, a peak array F={f1, f2,..., fN}, and a kurtosis value array Q={q1, q2,..., qN}, and then calculating average values and standard deviations of the average value array, the maximum value array, the minimum value array, the peak array, and the kurtosis value array, which are denoted as x avg, σ avg, x MAX, σ MAX, x MIN, σ MIN, x F, σ F, x Q, and σ Q respectively; an elimination step of determining whether following conditions are met for all heartbeat data in each piece of data: x avg - 3 σ avg < x avg < x avg + 3 σ avg, x MAX - 3 σ MAX < x MAX < x MAX + 3 σ MAX, x MIN - 3 σ MIN < x MIN < x MIN + 3 σ MIN, x F - 3 σ F < x F < x F + 3 σ F, and x Q - 3 σ Q < x Q < x Q + 3 σ Q, obtaining a number of the conditions satisfied, and eliminating heartbeat data satisfying less than three of the above conditions in initial data to form a new heartbeat dataset; a repetition step of repeating the statistics step until a heartbeat dataset does not change, and using the heartbeat dataset as training data; and an input step of inputting the training data and a corresponding tag to a convolutional neural network for training, to obtain a convolutional neural network model based on sample distribution improvement.