China has over 1.116 billion mobile Internet users, whose demands continue to evolve rapidly. However, existing Chinese spoken language understanding (SLU) benchmarks are outdated and cover limited domains, failing to reflect real-world mobile assistant scenarios. To this end, we introduce CMPA-SLU, a Chinese SLU dataset for mobile personal assistants collected from real-world applications and annotated with rigorous quality control. CMPA-SLU contains 50 domains, 247 intents, and 74 slot types, expanding the diversity of existing Chinese SLU benchmarks. To better simulate practical deployment scenarios, we further introduce a Partial Category setting, where candidate labels are dynamically filtered using a Bidirectional Guided Embedding (BGE)-based retrieval strategy. We further adopt a label mapping strategy for joint intent and slot prediction within a single generation process. We evaluate multiple large language models (LLMs) under both Full Category and Partial Category settings. Experimental results show that the Partial Category setting improves performance by reducing the candidate label space.