With the continuous deepening of rural population aging in China, it is urgent to enhance the intelligence level of rice production. Among various production stages, the demand for intelligence in the harvesting process is particularly pressing and challenging to achieve. Currently, due to the limited capacity of grain bins in intelligent rice harvesters, frequent unloading is required during operations, which significantly impacts the operational efficiency. To address this issue, this paper systematically reviews the research progress on key technologies of two collaborative operation modes, namely fixed-point unloading and vehicle-following unloading, by integrating the current research status at home and abroad. In terms of fixed-point unloading technology, through the application of high-precision spatial geometric modeling, parking distance compensation prediction, and stereo vision detection technology, precise alignment with longitudinal deviation less than 0.20 m and lateral deviation less than 0.10 m has been achieved, effectively improving the accuracy and efficiency of unloading. In the aspect of vehicle-following unloading technology, based on an improved inter-machine communication protocol (utilizing radio/4G dual-mode) and the Kalman filter delay compensation method, the communication error has been successfully reduced by over 82.00%. Meanwhile, combined with the gain self-adjusting single-neuron control algorithm, the dynamic collaborative longitudinal deviation is stably controlled within ±0.08 m, significantly enhancing the stability and reliability of vehicle-following unloading. Regarding path planning, this paper constructs a multi-objective optimization model based on an improved ant colony algorithm. Simulation results indicate that the adoption of this model can improve collaborative operation efficiency by 13.58%, further optimizing the harvesting process. By integrating the aforementioned technologies, the constructed collaborative system enables a rice harvesting efficiency of 0.42 hectares per hour, representing a 26.00% increase compared to single-machine operations. However, existing research still has certain limitations in terms of adaptability to complex farmland environments and applicability to irregular plots. In the future, it is necessary to further strengthen the system's robustness and conduct multi-scenario validation to promote the widespread application and development of intelligent rice harvesting technology.