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This study presents a hardware\u2010level multi\u2010dimensional encryption paradigm utilizing optoelectronic neuromorphic devices with low energy consumption of 3.3 fJ, exhibiting great potential in motion detection, in\u2010sensor computing and multilevel encrypted information communication. By encoding ASCII characters into unique optical pulse sequences defined by wavelength, duration, and pulse number, the device transforms digital information into physically obfuscated electrical responses, thereby establishing a secure encryption mechanism. Based on neuromorphic response of optoelectronic device, convolutional neural network was trained to decrypt signals with recognition accuracy of 97.4% for legitimate users while maintaining robustness against unauthorized access (\u223c2.88% accuracy). 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