diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py
index de3296efb7d6f..8f3a71e97510f 100644
--- a/python/paddle/tensor/linalg.py
+++ b/python/paddle/tensor/linalg.py
@@ -2930,7 +2930,7 @@ def svd(
     Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
 
     .. math::
-        X = U * diag(S) * VT
+        X = U * diag(S) * V^{H}
 
     Args:
         x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
@@ -3010,7 +3010,7 @@ def svdvals(x: Tensor, name: str | None = None) -> Tensor:
     produced by singular value decomposition:
 
     .. math::
-        X = U * diag(S) * VH
+        X = U * diag(S) * V^{H}
 
     Args:
         x (Tensor): The input tensor. Its shape should be `[..., M, N]`, where
@@ -3091,12 +3091,12 @@ def svd_lowrank(
     If :math:`X` is the input matrix or a batch of input matrices, the output should satisfies:
 
     .. math::
-        X \approx U * diag(S) * V^{T}
+        X \approx U * diag(S) * V^{H}
 
     When :math:`M` is given, the output should satisfies:
 
     .. math::
-        X - M \approx U * diag(S) * V^{T}
+        X - M \approx U * diag(S) * V^{H}
 
     Args:
         x (Tensor): The input tensor. Its shape should be `[..., N, M]`, where `...` is