A method for identifying tap events on a touch panel includes measuring vibration signals for tap events on the touch panel to collect the tap events and record types of the tap events as samples; generating a sample set including a plurality of samples; using the sample set to train a deep neural network to determine an optimized weighting parameter group; taking the deep neural network and the optimized weighting parameter group as a tapping classifier and deploying it to a touch-controlled end product; and obtaining a predicted tap type based on a vibration signal and an image formed by touch sensing values detected by the touch-controlled end product to which a tap operation is performed. The present disclosure also provides a system corresponding to the identifying method, and a touch-controlled end product.