At present,high-dimensional (HD) feature detection is an effective approach to improve the detection performance of sea-surface small targets.The main difficulty lies in the design of classifier in high-dimensional space.Therefore,a feature detection approach based on false-alarm-controllable gradient boosting decision tree (GBDT) is proposed in this paper.First,multiple features are extracted from the 1D long-term observation vector in time,frequency,time-frequency domains to construct an HD feature vector.In this way,the detection problem is converted into a binary classification problem.Second,two types of balanced training samples are solved by simulating returns with target.Third,GBDT algorithm is introduced to condense the HD feature vector into 1D predicted value in probability.The predicted value is used as detection statistics to solve the problem of uncontrollable false alarm rate perplexed the binary classifier.Finally,experimental results are verified by IPIX measured data,which show that the proposed detector can make full use of all the information from the HD characteristics,and the performance is improved by over 13%.