Distributed Acoustic Sensors (DAS) can effectively monitor acoustic fields along sensing fibers with high sensitivity and response speed. However, their data processing is limited by the performance of electronic signal processing, hindering real-time applications. The Time-Wavelength Multiplexed Photonic Neural Network Accelerator (TWM-PNNA), which uses photons for operations instead of electrons, significantly enhances processing speed and energy efficiency. Therefore, this work explores the feasibility of applying TWM-PNNA to DAS systems. It first discusses processing large DAS system data for compatibility with the TWM-PNNA system. It also investigates the effects of chirp on optical convolution in complex tasks and methods to mitigate its impact on classification accuracy. Furthermore, a method for achieving optical full connection is proposed, and the influence of pruning on the full connection is studied to reduce the computational burden of the model. Experimental results indicate decreasing the ratio of Δλchirp/Δλ or choosing push-pull modulation can eliminate the impact of chirp on recognition accuracy. Additionally, when the full connection parameter retention rate is no less than 60%, classification accuracy exceeds 90%. TWM-PNNA provides a novel computational framework for DAS systems, paving the way for the all-optical fusion of DAS systems with computational systems.