Planar cameras with high performance and wide field of view (FOV) are critical in various fields, requiring highly compact and integrated technology. Existing wide FOV metalenses show great potential for ultrathin optical components, but there is a set of tricky challenges, such as chromatic aberrations correction, central bright speckle removal, and image quality improvement of wide FOV. We design a neural meta-camera by introducing a knowledge-fused data-driven paradigm equipped with transformer-based network. Such a paradigm enables the network to sequentially assimilate the physical prior and experimental data of the metalens, and thus can effectively mitigate the aforementioned challenges. An ultra-wide FOV meta-camera, integrating an off-axis monochromatic aberration-corrected metalens with a neural CMOS image sensor without any relay lenses, is employed to demonstrate the availability. High-quality reconstructed results of color images and real scene images at different distances validate that the proposed meta-camera can achieve an ultra-wide FOV (>100 deg) and full-color images with the correction of chromatic aberration, distortion, and central bright speckle, and the contrast increase up to 13.5 times. Notably, coupled with its compact size (< 0.13 cm3), portability, and full-color imaging capacity, the neural meta-camera emerges as a compelling alternative for applications, such as micro-navigation, micro-endoscopes, and various on-chip devices.