Tactile intelligent perception is one of the hot issues in current researches. However, the lack of large-scale tactile data sets limits the development of the field of tactile perception. The key to solving the problem is to build a high-temporal-resolution tactile pressure sensor system that covers the palm. This paper constructs a brain-inspired tactile sensing system(BITSS), which acquires tactile pressure information with high spatiotemporal resolution and realizes tactile perception based on spike events. Inspired by the skin tactile receptors, the BITSS uses the neuromorphic model to encode spike the tactile pressure signals and realizes the simulation of two types of tactile receptor neurons. The experimental results show that the neuroelectric activity simulated by the BITSS can decode the low-dimensional space of the grasping state. In the classification tasks of ten daily objects, we provide a spike-based Bayesian classifier with a classification accuracy of 94% and a fast execution speed. The above results verify the spatiotemporal coding ability of the BITSS for tactile pressure signals.