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With The Ever-increasing Amount Of Data Resided In A Cloud, How To Provide Users With Secure And Practical Query Services Has Become The Key To Improve The Quality Of Cloud Services. Fuzzy Searchable Encryption (FSE) Is Identified As One Of The Most Promising Approaches For Enabling Secure Query Services, Since It Allows Searching Encrypted Data By Using Keywords With Spelling Errors. However, Existing FSE Schemes Are Far From The Practical Use For The Following Reasons: (1) Inflexibility. It Is Hard For Them To Simultaneously Support AND And OR Semantics In A Multi-keyword Query. (2) Inefficiency. They Require Sequentially Scanning A Whole Dataset To Find Matched Files, And Thus Are Difficult To Apply To A Large-scale Dataset. (3) Limited Robustness. It Is Difficult For Them To Resist The Linear Analysis Attack In The Known-background Model. To Fix The Above Problems, This Article Proposes Matrix-based Multi-keyword Fuzzy Search (M2FS) Schemes, Which Support Approximate Keyword Matching By Exploiting The Indecomposable Property Of Primes. Specifically, We First Present A Basic Scheme, Called M2FS-B, Where Multiple Keywords In A Query Or A File Are Constructed As Prime-related Matrices Such That The Result Of Matrix Multiplication Can Be Employed To Determine The Level Of Matching For Different Query Semantics. Then, We Construct An Advanced Scheme, Named M2FS-E, Which Builds A Searchable Index As A Keyword Balanced Binary (KBB) Tree For Dynamic And Parallel Searches, While Adding Random Noises Into A Query Matrix For Enhanced Robustness. Extensive Analyses And Experiments Demonstrate The Validity Of Our M2FS Schemes.