Abstract: Browser fingerprinting is a user identification method which has gradually matured since its concept was proposed in 2010 and is widely used in a lot of popular business websites to serve ads accurately. However, traditional fingerprinting has lots of problems in tracing users because it changes subtly no matter if the fingerprint feature value is changed due to system upgrade, browser update or tampering caused by fingerprint blocker. On the basis of research on the attributes of browser fingerprint, a great number of fingerprints from the volunteers who used Android devices are collected and supervised learning framework RNNBF for user identification is proposed. The robustness of RNNBF is reflected in the data and the model respectively. In the data aspect, the fingerprint-based data enhancement technology is used to generate the enhanced data set. In the model aspect, the attention mechanism is used to make our model focus more on the invariant fingerprint features. In terms of model evaluation, the RNNBF model is compared with the single-layer LSTM model and the random forest model. When F1-Score is used as the evaluation standard, the recognition effect of the RNNBF model is better than the latter two, which proves the excellent performance of RNNBF in dynamically linking fingerprints.