The invention discloses a CNN-LSTM multi-
feature fusion-based
lipocalin classification method, which comprises the following steps: S1, constructing a
data set, and dividing the
data set into training data,
verification data and
test data; s2, extracting
protein sequence features by using three feature descriptors of K-mer, CKSAAP and CC-PSSM, and fusing the
protein sequence features through a feature integration technology; s3, performing
dimensionality reduction optimization on the fused features by using
principal component analysis (PCA), and reserving features with a variance proportion threshold value of 0.98 to obtain low-dimensional important features; s4, the features subjected to dimension reduction are input into a CNN-LSTM fusion model, CNN comprises 128 filters, the size of a
convolution kernel is 1, an
activation function is ReLU, LSTM comprises 64 units, and a category probability is output through combination of a full connection layer and a
Softmax function; and S5, performing classification prediction on the lipid
protein according to the output probability value. According to the method, the problems of insufficient characteristic utilization and high calculation cost of a traditional method are solved, and an efficient tool is provided for lipid
protein function research and
disease diagnosis.