Abstract: Sensitive and efficient analysis of microplastics (MPs) in environmental media provides essential support for monitoring MPs and informing preventive measures. To address the current challenges in MPs detection, such as complex sample pretreatment steps and insufficient automated batch processing, a method that integrates pre-pyrolysis with automated solid-phase microextraction (SPME) and gas chromatography-mass spectrometry (GC-MS) for extracting and analyzing six microplastics including polypropylene (PP), polystyrene (PS), polyvinylidene chloride (PVDC), polyvinylidene fluoride (PVDF), polyethylene (PE), and polyethylene terephthalate (PET) in aquatic samples was proposed based on the Gerstel Multi-Purpose Sampler platform. Results demonstrate that MPs undergo efficient pyrolysis after 20 minutes at 450℃ in a self-constructed preheating pyrolysis apparatus. SPME of the pyrolysis products was performed using a commercial PDMS/DVB/CAR-coated fiber, and the pyrolysis products of six MPs were identified by GC-MS. Subsequently, polymer-specific marker compounds were selected for each MPs based on differences in the structures of their pyrolysis products. The effects of pyrolysis temperature, pyrolysis time, extraction temperature, and extraction time on extraction efficiency were optimized. Under optimal conditions, the method exhibited excellent linear ranges, with PP, PS, PVDC, and PVDF having a range of 1–10000 ng/L, and PE and PET having a range of 10–10000 ng/L. The detection limits and quantification limits of the method were 0.046–3.5 ng/L and 0.16–12.1 ng/L, respectively, with relative standard deviations ≤12.9%. The method was applied to the determination of MPs in real water samples, with recoveries ranging from 75.2% to 116.4%. In this study, batch pyrolysis was integrated with automated SPME technique to achieve the integrated extraction and concentration of pyrolysis products from MPs. The approach streamlined pretreatment and substantially improved detection sensitivity and throughput, offering a novel, efficient method for detecting MPs in environmental matrices.