Traditional data extraction methods are usually inefficient. To address this problem, we first design an exact position addressing-based algorithm with multi-processing methods to achieve the accurate positioning of data blocks by taking the massive data generated from semi-structured numerical weather prediction (NWP) products as the research object. Then, an extraction algorithm is designed to extract data in the spatial range on demand, namely, to realize on-demand data extraction according to attribute dimensions as well as the latitude and longitude of data. As a result, the multi-process data reading under unified whole-process control is achieved on the basis of the above two algorithms. For testing, the time consumption of a single data plane is taken as the main assessment index, and the single-, quad-, octo-, and 16-core processes are employed for data processing. The test results reveal that the processing with 16-core processes is faster than that of a single-core process, and the time consumption is reduced from 257 ms to 37 ms. This method can effectively improve the efficiency of data extraction for non-structural NWP products and has been put into use in decision analysis for urban governance.