To address the problem that artificially extracted redundant feature sets and irrelevant feature sets lead to the degradation of human activity recognition classification performance of wearable sensor， this paper proposes a human activity recognition method based on heuristic integrated feature selection. The method first selects the feature set containing power spectral density （PSD） for recognizing confusing activities. Then， on this basis， the method screens out the lowly correlated feature subsets with the help of Pearson correlation coefficient （PCC） method， then uses an improved sine cosine algorithm （SCA） for features and obtains the optimal feature subset by screening the feature twice. The experimental results show that the feature subset dimension after using this method in the data set collected in the laboratory is 34， and the recognition accuracy rate reaches 98.21%. In the public SCUT-NAA data set for comparison experiments， the feature subset dimension is 39， lower than the feature dimension of previous research methods， and the recognition accuracy rate reaches 96.51%.