Raman spectroscopy is a type of inelastic scattering that provides rich information about a substance based on the coupling of the energy levels of their vibrational and rotational modes with an incident light. It has been applied extensively in many fields. As there is an increasing need for the remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact, easily transportable, and fully automated remote Raman detection system for trace detection and identification of information, with high-level confidence about the target's composition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system provides good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a convolutional neural network (CNN)-based algorithm is implemented and packaged into the recognition software to achieve faster and more accurate detection and identification. This prototype offers a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.