In conventional infrared device systems, the von Neumann architecture, which integrates sensors, processors, and memory, faces limitations due to high power consumption and high latency. Drawing inspiration from mammalian bipolar cells, this paper designs a metal-semiconductor-metal (MSM) structured infrared sensing-computing integrated device utilizing black phosphorus-indium arsenide-black phosphorus (BP-InAs-BP) as the semiconductor material. This device achieves symmetric positive/negative photoresponses and amplitude modulation across both infrared and visible light spectra through bias adjustment, enabling integrates perception and computation at the detector level. Extensive fittings of physical models facilitate the establishment of a physical simulation model for this detector, allowing microscopic-level analysis of its design, including theoretical derivation of energy band structures and micro-scale charge distributions. This significantly advances the development of BP-based infrared detector applications. In convolutional neural network tests based on this design, classification accuracy for digits 0?9 classification tasks in MNIST dataset with scale of 16 pixel×16 pixel exceeded 92%, highlighting the superior performance of this proposed infrared sensing-computing device. The simulation methodology presented in this study provides a novel design framework and theoretical analysis approach for BP material applications, offering a practical solution for neuromorphic visual perception.