Near field radiative heat transfer provides a route for information processing in which thermal radiation, rather than charge transport, serves as the physical carrier of signals. Here, we propose and theoretically analyze a programmable near field radiative thermal computing framework in which radiative coupling, phase change nonlinearity, and thermal state memory are mapped onto neural network inspired operations. The framework is constructed from near field radiative thermal diodes, transistors, and multi terminal logic units separated by nanoscale gaps. Radiative heat flux represents the propagated thermal information, while geometry and material dependent radiative coupling provides physically constrained weighting, and the temperature dependent optical response of phase-change materials enables nonlinear modulation and logic state control. Based on these primitives, we formulate a radiative thermal convolutional network for spatial information processing and a radiative thermal recurrent network for history dependent computation. The recurrent response is associated with radiative feedback, thermal relaxation, and phase change hysteresis, with VO2 providing history dependent short term memory and GST offering a possible route toward non volatile phase storage. We further distinguish the physical radiative networks from a separate software based inverse identification study, in which recurrent machine-learning models are trained on simulated near field heat flux temperature characteristics to recover structural parameters. By establishing a bottom up connection between fluctuational electrodynamics, radiative thermal logic, programmable thermal states, and neural network inspired computation, this study provides a physically grounded basis for exploring non contact thermal information processing at the near field limit.