Under the condition of limited resources, when the GIS service platform is faced with high concurrent service requests, the high concurrent processing capacity of a single server is the main bottleneck affecting the quality of service (QoS) of the GIS service platform. Traditional traffic shaping methods, such as token bucket algorithms, alleviate the pressure in high-concurrency situations to a certain extent, but due to their fixed-rate settings, it is difficult to cope with complex and variable task requests, leading to unstable system performance. This paper proposes an adaptive task traffic shaping method to improve the utilization efficiency of server resources, reduce task processing delay, and improve service stability and response speed by monitoring the system load in real time and dynamically adjusting the processing rate of GIS task traffic. The relationship between the task arrival rate and server load is established based on the queueing theory model, and the token fill rate of the token bucket is adjusted adaptively according to resource load evaluation, so as to optimize the task processing flow. The experimental results show that under different GIS task scenarios, the processing performance of this paper’s method is better than or close to that of the traditional method under the optimal fill rate condition, which can significantly reduce the task latency and improve the concurrent processing capability of the GIS service platform.