<p>The widespread use of GPS-enabled mobile devices has resulted in a rapid increase in spatial text data, driving the growth of cloud-based spatial-keyword query services. However, reliance on untrusted cloud servers introduces serious challenges regarding data privacy and result integrity. To address these issues, we propose a verifiable privacy-preserving spatial-keyword range query (VPSRQ) scheme. We design a novel vectorization framework that simultaneously reduces vector dimensionality and enables efficient privacy-preserving query processing. Then we introduce a Homomorphic Inner Product Encryption (HIPE) scheme based on bilinear pairing, which allows secure inner product computation over encrypted vectors without leaking plaintext information. Besides, we construct a keyword object inverted index (KOI-index) and integrate it with a BP accumulator, providing both high query efficiency and lightweight verifiability of results. Through formal security analysis under the real-world/ideal world framework and comprehensive experiments on real-world datasets, VPSRQ is shown to significantly improve query performance and verification efficiency, while rigorously preserving the privacy and integrity of query results.</p>