As an essential type of observability data, distributed tracing data plays a crucial role in operation and maintenance tasks like performance analysis, fault diagnosis, and system understanding. Due to the rapid increase in system scale and complexity, the volume of tracing data grows exponentially, putting forward higher storage requirements. To mitigate the storage cost of tracing data, data compression becomes a crucial approach. Existing compression methods fail to fully exploit tracing data features for achieving efficient compression, and they do not support complex queries on compressed data either. This study introduces a neural-network-based approach for compressing and querying distributed tracing data. It employs a novel redundancy extraction technique to identify pattern and structural redundancies within tracing data, and leverages neural network models and arithmetic coding to achieve efficient data compression. Meanwhile, the method enables efficient querying of compressed data without decompressing all the data. Variously sized tracing datasets are collected from four open-source microservices systems, and the proposed method is evaluated. Experimental results show relatively high compression ratios (105.5–197.6) are achieved by the proposed method, which are four times those of state-of-the-art general compression algorithms on average. Additionally, the querying efficiency of the proposed method on the compressed data is validated, showcasing faster performance than existing query tools in optimal scenarios.