Aiming at the problem that existing symbolic regression methods are difficult to effectively generalize to sequences in the On-line Encyclopedia of Integer Sequences （OEIS）， a symbolic regression method for integer sequence based on Self-Learning （SL） was proposed. Firstly， a variety of learning data were constructed through programs， and integrated into high-order linear recursive data according to the characteristics of OEIS data， and the OEIS initial term was used to generate recursive sequences. Secondly， the learning data were converted into OEIS data， and a strategy of fusing multiple OEIS data as the data of initial iteration was proposed. Finally， the formulas of the OEIS sequences were gradually discovered through self-learning iteration. The iteration process was divided into four stages： Learn， Search， Check and Select. Experimental results show that the proposed method is better than the Deep Symbolic Regression （DSR） method and Mathematica’s built-in function. Compared with the DSR on the three test sets of Easy， Sign and Base， the accuracy of the proposed method improved by 9.66， 4.17， and 5.14 percentage points respectively. A total of 27 433 formulas of the OEIS sequence were found. The newly discovered formulas can assist mathematicians in conducting related theoretical research.