Heart rate variability （HRV） is widely used in clinical autonomic nervous system assessment and classification of abnormal heart rate. Traditional HRV analysis is based on electrocardiogram （ECG）， photoplethysmography （PPG） and remote PPG （RPPG）. However， these methods have the following disadvantages： （1） The detection of ECG requires the application of irritating coupling agent on the skin and additional electrodes， which is not suitable for long-term monitoring， and the ECG equipment is expensive； （2） there is ambient optical noise in the PPG and RPPG measurement， and individual difference due to skin color is obvious； （3） the detections of ECG and PPG belong to contact type， which can easily bring discomfort to patients. Based on the shortcomings of the above methods， a HRV analysis method based on ballistocardiogram （BCG） is proposed. It reduces the cost of traditional equipment for HRV analysis， and uses non-contact detection to alleviate the discomfort of patients. The unique detection principle avoids the problem of individual differences， which plays a vital role in long-term cardiovascular disease prediction. In the experiment， the model of back propagation （BP） neural network is used to predict and classify abnormal heart rate with an accuracy rate of 80%， showing the advancement and reliability of the proposed method.