Multi-mode fiber (MMF) has emerged as a promising platform for spatial information transmission attributed to its high capacity. However, the scattering characteristic and time-varying nature of MMF pose challenges for long-term stable transmission. In this study, we propose a spatial pilot-aided learning framework for MMF image transmission, which effectively addresses these challenges and maintains accurate performance in practical applications. By inserting a few reference image frames into the transmitting image sequence and leveraging a fast-adapt network training scheme, our framework adaptively accommodates to the physical channel variations and enables online model update for continuous transmission. Experimented on 100 m length unstable MMFs, we demonstrate transmission accuracy exceeding 92% over hours, with pilot frame overhead around 2%. Our fast-adapt learning scheme requires training of less than 2% of network parameters and reduces the computation time by 70% compared to conventional tuning approaches. Additionally, we propose two pilot-insertion strategies and elaborately compare their applicability to a wide range of scenarios including continuous transmission, burst transmission and transmission after fiber re-plugging. The proposed spatial pilot-aided fast-adapt framework opens up the possibility for MMF spatial transmission in practical complicated applications.