The subset sum problem is an important problem in computer science and the basis for constructing many public key cryptosystems. A random sampling method is proposed, so the dimension of the problem can be reduced by decomposing the original problem into multiple smaller subsets sum problems, reducing the radius of the constructed lattice, thereby improving the efficiency of solving SVP, and then reaching the solution. The theoretically worst-case success rate of the algorithm is given, and a possible method to improve the success rate of the algorithm is given based on the 0-1 distribution of the solution vector, when the weight of target solution is low, the solution vector is divided into sectors, thus applying restrictive conditions to the problem to improve the efficiency of problem-solving. Experimental results show that for high-dimensional subsets and problems, compared with the existing lattice reduction subsets and problem methods such as CJLOSS, the proposed algorithm can solve the exact solution of the problem more efficiently, and can improve the approximation degree of the approximate solution, the average length of the output approximate solution is 0.55 times that of the CJLOSS algorithm and 0.64 times that of the DR algorithm.