To solve the problem of inefficient calculation and analysis of massive seismic data in a single machine environment, we propose a distributed architecture based method for storage, calculation, and analysis of seismic data and select the complex calculation process of a noise power spectrum as the application scenario for implementation. In light of Hadoop’s performance advantage in massive data processing, the storage and scheduling of seismic data are carried out on the Hadoop Distributed File System (HDFS). The implementation of the quality evaluation method for the noise power spectrum of seismic data in Spark distributed computing architecture is studied. The elastic dataset Spark RDD is used to automatically allocate the tasks to the computing nodes, and the seismic waveform data stored in HDFS is analyzed. In addition, the calculation results are input into the distributed database HBase in the RowKey mode, realizing the storage and extraction of the power spectra of long-period seismic noise. The calculation results show that the method based on Spark distributed architecture can support the efficient processing of massive data at the TB level in volume, which can be applied to the analysis and calculation of massive seismic data.