The soundscape contains important ecological information, with features of real-time and high information density. It has significant research value and has been paid more and more attention by ecologists in recent years. Nowadays, in the soundscape research field, capturing and analyzing the audio and the related environmental parameters is still time and labor-consuming work, which throttles further research on the soundscape. We built a soundscape big data online capturing and analysis system based on state-of-the-art technology, such as multi-sensor, edge computing, and deep learning to remove the barrier. The system consists of edge computing units and central computing servers. In order to verify the stability and reliability of this system, we performed one-year technical confirmation by setting up three field research sites to automate the capturing, transmission, and analysis of soundscape big data. The system could stably and continuously complete the scheduling task, including online capturing and analysis of soundscape big data. The system can still work correctly in a harsh environment. The calculation results of the acoustic index show that the acoustic index can reflect the soundscape change. The variation pattern of these acoustic indexes was different in the same acoustic environment, caused by the different emphasis of the acoustic indexes. We suggest the researcher combine these acoustic indexes to explain the soundscape variation. The voiceprint map extracted from the audio file transmitted back by the edge computing unit can directly identify different sound sources and is beneficial for rapid species identification and sound source classification. The high dimensional soundscape features that the analysis system extracted from audio using VGGish-Net are commendably able to distinguish the change of soundscape in different times and locations, which has the potential to quickly and intuitively reflect the types and dynamic changes of diverse ecosystems. Especially with the extracted high dimensional soundscape features with the help of random forest classifier has high distinguished degree at different research sites and day-night scale. Enriching the voiceprint feature database, optimizing the neural network for soundscape feature analysis, and building a shared network for long-term soundscape monitoring will be beneficial for expanding the application of the system in species identification, rapid biodiversity analysis, and the interaction mechanism exploration between creatures and environment. This study provides a detailed reference for the big data online capturing and analysis of soundscape.