The invention discloses a 
random forest parallelization 
machine studying method for 
big data in a Spark cloud service environment. The method comprises the steps that dimension reduction 
processing is performed on the high-dimensional 
big data through 
feature vector importance analysis, and prediction is performed by adopting a weighed voting mode; through a 
distributed memory management mechanism and a 
cloud computing platform, parallelization of 
random forest training process 
model building, single decision-making tree splitting process and prediction voting is improved. According to the method, dimension reduction 
processing is performed on the high-dimensional 
big data through 
feature vector importance analysis, prediction is performed by adopting the weighed voting mode, therefore, optimization of the 
random forest method is achieved, and the mining effect of the random forest 
machine studying method on the complex big data is improved; the random forest parallelization method based on the Spark cloud platform is performed on the basis, so that the operation efficiency of the random forest 
machine studying method is improved.