Nowadays, the method of simple-feature extraction has been extensively studied and is used in PPG biometric recognition; some promising results have been reported. However, some useful information is often lost in the process of PPG signal denoising; the time-domain, frequency-domain, or wavelet feature extracted is often partial, which cannot fully express the raw PPG signal; and it is also difficult to choose the appropriate matching method. Therefore, to make up for these shortcomings, a method of PPG biometric recognition based on multifeature extraction and naive Bayes classifier is proposed. First, in the preprocessing of the raw PPG data, the sliding window method is used to rerepresent the raw data. Second, the feature-extraction methods based on time-domain, frequency-domain, and wavelet are analysed in detail, then these methods are used to extract the time-domain, frequency-domain, and wavelet features, and the features are concatenated into a multifeature. Finally, the multifeature is normalized and combined with classifiers and Euclidean distance for matching and decision-making. Extensive experiments are conducted on three PPG datasets, it is found that the proposed method can achieve a recognition rate of 98.65%, 97.76%, and 99.69% on the respective sets, and the results demonstrate that the proposed method is not inferior to several state-of-the-art methods.