Root Cause Analysis （RCA） is of critical importance in aiding rapid system recovery， accurately assessing risks， and ensuring production safety. Addressing the limitations of current RCA methods， which struggle to adequately characterize dependencies among different sensors and fail to capture stochastic fluctuations in time series， a new approach called GPRCA for multi-dimensional time series root cause analysis using a probabilistic generative graph attention network was proposed. This method regarded dimension feature embeddings as Gaussian distribution vectors to characterize latent features of different sensors， effectively capturing stochastic fluctuations in multi-dimensional time series and enhancing the model robustness against noise. Simultaneously， a deep probability generative graph attention network was constructed to learn the nonlinear dependencies between dimensions， thereby effectively modeling dependencies within sensor networks. Finally， the root cause analysis was conducted by integrating the network topology causal score and the individual node causal scores. Experimental results on two public datasets （SWaT and WADI） and one private dataset （Mine） showed that GPRCA achieved the optimal values on certain metrics. Specifically， on the SWaT dataset， GPRCA improved 2.2%， 6.3%， and 11.6% on P@5 （Precision）， mAP@5 （mean Average Precision）， and Mean Reciprocal Rank （MRR）， respectively，compared to the sub-optimal method； on the WADI dataset， GPRCA improved 8.1%， 7.0%， and 11.0% on P@5， mAP@5， and MRR， respectively， compared to the sub-optimal method. On the Mine dataset， GPRCA improved 3.6% and 1.8% on mAP@3 and MRR， respectively， compared to the sub-optimal method. It can be seen that GPRCA method has the effectiveness and better performance than the baseline methods.